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Choose one of the scenarios below to prepare a voice-over PowerPoint presentation

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Choose one of the scenarios below to prepare a voice-over PowerPoint presentation.

Your target audience includes newly minted MSWs to whom you are providing training on social work research methods for their practice. Presentations provide a key form of communication in the social work field and will be used throughout your program. You may already find you use presentations in your work. Presentations are used for many different kinds of conference sessions, from panels to posters. Presentations are also used in training and teaching in all settings. They provide an invaluable tool during webinars, seminars, and other training and information-sharing venues. Presentations are used to propose projects, show a status of progress, and defend ideas. Be sure to review the NCU Guide to Creating a Successful PowerPoint presentation located in this week’s resources.

Scenario 1: Assume you are a social worker supervisor for a young adult men’s forensic facility for individuals 18 to 24 years old. You have noticed that there is an unspoken rank and file among the residents (for instance, certain residents are allowed to cut in the lunch line while others are not) as well as a somewhat secretive code of behavior that staff has observed. You are interested in better understanding the culture of this facility through the eyes of your social work staff, who provide case management to the residents. You have weekly supervision appointments with each of your case managers (this is your sample) and plan to interview the staff members on resident culture during the individual weekly meetings with you.

• Determine the questions you would ask.
• Indicate if these questions lend themselves to quantitative or qualitative methods.
• Identify the research design you would choose.
• Identify and discuss any ethical concerns and how you would overcome these concerns.
• Explain your role as the researcher.
• Describe how you would record their information and protect their confidentiality.

Scenario 2: Your agency provides emergency food and used clothing to clients. You are asked to describe your clients’ level of satisfaction regarding the services that they received through your agency.

• List the questions that you would ask to collect information on clients’ satisfaction with these services.
• Explain how you would collect data using a quantitative method.
• Identify and discuss any ethical concerns and how you would overcome these concerns.
• If you were to use a cross-sectional design, determine which method(s) you would use to collect information, and then explain why.

Incorporate appropriate animations, transitions, and graphics as well as speaker notes for each slide. The speaker notes may be comprised of brief paragraphs or bulleted lists and should cite material appropriately. Add audio to each slide using the Media section of the Insert tab in the top menu bar for each slide.

Support your presentation with at least three scholarly resources. In addition to these specified resources, other appropriate scholarly resources may be included.

Length: 12-15 slides (with a separate reference slide)
Notes Length: 100-150 words for each slide

Be sure to include citations for quotations and paraphrases with references in APA format and style where appropriate. Save the file as PPT with the correct course code information.

Skip to main contentSkip to article • Journals & Books • • RegisterSign in Brought to you by:Northcentral University Download PDF Outline 1. Evaluating Differences 2. Examining Relationships 3. Making Predictions 4. Interpretation 5. Conclusion 6. References Show full outline Tables (1) 1. Table 1 Air Medical Journal Volume 28, Issue 4, July–August 2009, Pages 168-171 Basics of Research Part 15 Inferential Statistics Author links open overlay panelShaneAlluaPhD1Cheryl BagleyThompsonPhD, RN2 Show more Add to Mendeley Share Cite https://doi.org/10.1016/j.amj.2009.04.013Get rights and content This article is the 15th in a multipart series designed to improve the knowledge base of readers, particularly novices, in the area of clinical research. A better understanding of these principles should help in reading and understanding the application of published studies. It should also help those involved in beginning their own research projects. • • Previous article in issue Next article in issue Descriptive statistics (see part 13 in this series1) summarize the data with the purpose of describing what occurred in the sample. In contrast, inferential statistics are calculated with the purpose of generalizing the findings from a sample to the entire population of interest. For instance, an investigator would use inferential statistics to determine whether differences between groups (ie, treatment and control groups) are unique to his or her sample (because of chance) or are a result of real differences between the population represented by group 1 and the population represented by group 2 (or however many groups are involved). Inferential statistics, therefore, rely on appropriate sampling methods (see part 5 of this series2) to ensure maximal representation of the population of interest. Inferential statistics are based on probability theory and the process of hypothesis testing (see part 14 in this series3). Inferential statistics can be classified as either parametric or nonparametric. Nonparametric statistics are most commonly used for variables at the nominal or ordinal level of measurement, which basically means that they are used for variables that do not have a normal distribution. Statistical significance is calculated using information contained only in the sample (rather than the population) and may use measures of central tendency appropriate for nominal or ordinal level data (ie, the median rather than the mean). Parametric statistics are the most common approach to inferential statistical analysis. Parametric statistics require that the variables be measured at the interval or ratio level. Use of parametric statistics also relies on other assumptions, such as the expectation that values for a given variable will be normally distributed in the population. Inferential statistics encompass a variety of statistical significance tests that investigators can use to make inferences about their sample data. These tests can be divided into three basic categories depending on their intended purpose: evaluating differences, examining relationships, and making predictions. The decision of which procedure to use is determined, in part, by the investigator's research question or research design. Level of measurement of the data (see part 13 in this series1) is also an important determinant in choice of significance test. Table 1 summarizes commonly used parametric and nonparametric statistical analyses. Research questions addressed by more commonly used parametric inferential statistics are discussed, followed by a discussion of statistical reporting and interpretation. Brief mention is made of common nonparametric equivalent statistical tests. However, the reader may want to consult with a statistician or other resources for further information on these as well as other advanced statistical procedures.4 Table 1. Common Statistical Techniques Statistical Test Non Parametric Independent Variable Dependent Variable Comments Statistical Test Independent Variable Dependent Variable Comments Chi-Squared Nominal Nominal Need > 5 expected subjects/cell Fisher's Exact Nominal Nominal Mann-Whitney U Dichotomous Ordinal Kruskal-Wallis Nominal Ordinal Spearman Rho Ordinal Ordinal t-Test Dichotomous Interval/Ratio ANOVA Nominal Interval/Ratio MANOVA Nominal Interval/Ratio 3 or more values for Independent Variable Parametric Multiple Dependent Variables Pearson's r (Correlation) Interval/Ratio Interval/Ratio Simple Regression Interval/Ratio Interval/Ratio Single Independent Variable Multiple Regression Interval/Ratio Interval/Ratio Multiple independent variables Canonical Interval/Ratio Interval/Ratio Multiple Independent and dependent variables Evaluating Differences Significant difference tests can be used to evaluate differences on one interval or ratio level dependent variable of interest between two groups and three or more groups. Two Groups Research question: Is there is a significant difference in the mean flight time for trauma and nontrauma patients? The independent samples t-test is used to test the statistical significance of the differences in means between two groups (a dichotomous independent variable) on some dependent variable measured at the interval or ratio level. For example, in an investigation of transport times, a t-test can be used to determine whether there is a significant difference in mean flight time for trauma and nontrauma patients. The t is the actual test statistic that is calculated and compared with the critical values of t that mark the critical region(s) indicating the presence of statistical significance. The critical t value is determined by the researcher-selected significance level or alpha level (eg, α = .05) and the degrees of freedom that represent the conditions under which the t is calculated (related to numbers of subjects, number of groups, and the statistic). The P-value is the probability of whether the differences seen in the two flight times is present because of a true difference (in population means) or because of chance (seen only in this sample, not the population). If the calculated t value falls within the critical region and thus P < .05, the null hypothesis is rejected in favor of the research hypothesis. Three or More Groups Research question: Is there is a significant difference in mean systolic blood pressure for the control group, the group with drug A, the group with drug B, or the group with both drugs A and B? An analysis of variance (ANOVA) is slightly more complex than a t-test but is based on the same mathematical principles. In fact, when an ANOVA is calculated for a two-group independent variable, the conclusions (significance vs nonsignificance) are exactly the same as the results obtained with a t-test. Although ANOVA can be used with two groups, it is most commonly used for independent variables that have three or more groups (possible values for the independent variable). Again, the dependent is assumed to be measured at the interval or ratio level. For ANOVA, the statistic calculated is an F, rather than a t. The F statistic is the value compared against the critical value of F, which defines the critical region to determine statistical significance. The P-value is interpreted exactly the same as for a t-test such that if the P-value is below the selected alpha and therefore the obtained F value falls within the critical region, the null hypothesis can be rejected. With an ANOVA, a statistically significant P-value indicates that there are group differences present in the data but does not indicate which groups are different. Thus, in an analysis of four treatment groups, if group 4 has much higher blood pressure than any of the other three groups, the investigator cannot assume that this specific difference is statistically significant. Further analysis (post hoc analysis) is needed to determine the nature of the differences. Post hoc analysis is beyond the scope of this paper. Both the t-test and ANOVA noted above assume independent samples or groups. For dependent samples where, for example, the same sample is measured at time 1 and time 2, a dependent samples t-test or repeatedmeasures ANOVA (time 1, time 2, and time 3) should be used. Furthermore, evaluation of group means can be done for multiple independent variables (factorial ANOVA) and multiple dependent variables (multivariate analysis of variance, MANOVA). Other nonparametric statistical tests used to evaluate group differences include the Mann-Whitney U test, the Wilcoxon T test, and the Kruskal-Wallis test. Examining Relationships Statistical tests also evaluate the significance of the relationship between two variables and the strength of the relationship. A correlation, however, cannot be used to infer a causal relationship between two variables. In other words, an investigator should only draw the conclusion that a relationship between two variables does or does not exist, not which variable is the cause of the other variable. Research question: Is there is a positive relationship between scene time and flight time? The most common statistic used to describe the relationship (the correlation) between two variables is the Pearson product-moment correlation or Pearson's r (rp. Pearson's r is a descriptive statistic when used only to describe a relationship; it is an inferential statistic when used to infer a relationship in the population. Pearson's r requires that both variables be measured at least at the interval level of measurement. Consequently, a correlation between role and age is not appropriate, even if role is assigned a numerical value (1 = nurse, 2 = physician). Pearson's r is used to evaluate both the statistical significance of the relationship and the magnitude and direction of the relationship. Pearson's r ranges from −1.0 to +1.0: a +1.0 indicates a perfect direct (positive) relationship, and a −1.0 indicates a perfect inverse (negative) relationship. Pearson's r of +1.0 or −1.0 are both represented as a straight line when displayed graphically. The further the data are from a perfect relationship, the more the points spread out from a straight line. One assumption of correlation is that a linear relationship exists between variables. To the extent that there is a nonlinear relationship between the two variables being correlated, correlation will understate the relationship. Similar to both the t and F statistic, the obtained rp is compared against the critical r value to define the critical region. If the obtained rp falls within the critical region and thus the P-value is less than the selected alpha level, the investigator can conclude that there is a statistically significant relationship between the variables. The rp also informs the investigator about the strength and direction of the relationship. For example, if rp = +0.37, there exists a moderate, positive relationship between scene time and flight time, meaning that as scene time increases, so does flight time. It does not mean that an increase in scene time causes an increase in flight time or vice versa. When data are not measured at the interval or ratio level, other variations of correlations are more appropriate. For example, the correlation between two ordinal level variables should be analyzed using the Spearman rho correlation coefficient, the nonparametric equivalent to the Pearson r. A common nonparametric statistic used to examine the relation between nominal level variables is the chi-squared test of association. Research question: Is there a relationship between intubation success and use of neuromuscular blockade? Data analyzed using the chi-squared test statistic, χ2, are typically organized into a contingency table. The chi-squared procedure calculates the expected number of observations in each cell of the contingency table and compares them with the number of observations actually occurring in each cell (observed frequencies). The greater the deviation of the observed frequencies from the expected frequencies, the greater the chance for statistical significance, providing evidence that the variables are related. In contrast to the correlations described, the interpretation of the χ2 does not include the direction of the relationship (ie, positive or negative). Rather, it is described by looking at the pattern of observed frequencies in the contingency table and identifying which categories of one variable go with which categories of the other variable. For example, given χ2 = 5.3, which is determined to be statistically significant at the .05 level, a statistically significant relationship between intubation success and use of neuromuscular blockade exists. Thus, evidence supports the conclusion that the use of neuromuscular blockade increases the intubation success rate. Making Predictions Although correlation does not allow the investigator to infer causation, other statistical tests can be used to predict one variable from another. Research question: Do weight, height, and smoking status influence resting pulse rate? Simple regression analysis is used for a single independent and dependent variable, whereas multiple regression analysis is used for multiple independent variables. Multiple regression assumes that the dependent variable is measured at the interval or ratio level. Regression analysis is computationally related to ANOVA but is used for independent variables that are measured at the interval or ratio level, rather than nominal or ordinal level. Interval or ratio level independent variables can be broken down into ordinal categories and analyzed using ANOVA (eg, ages 20–39 = 1, ages 40–59 = 2, ages 60–79 = 3). However, because a great deal of information is lost with this approach, regression analysis is preferred for interval or ratio-level independent variables. Statistical significance obtained using regression is somewhat different from that described for the statistical tests presented thus far. First, to determine whether the overall model or, if together, all independent variables are significant predictors or the dependent variable, the F statistic is evaluated as described previously. In addition, regression provides the investigator with a measure of predictive accuracy that is determined by the strength of the relationship. The predictive accuracy or R2 is the percentage of variance in the dependent variable explained by the independent variable(s). Another way to say this is that the R2 is the percentage of overlap between the independent and dependent variables. For example, given a significant F value, the investigator would conclude that, given R2 = 0.3064, approximately 31% of the variation in pulse rate can be explained by weight, height, and smoking status. Regression is also used to determine the significance and importance of each predictor or each independent variable in the model. The discussion of regression coefficients (eg, beta weights) is beyond the scope of this paper. By convention, a small r2 is used when referring to a single independent variable (eg, correlation), and a large R2 is used for multiple independent variables. Reporting the adjusted R2 is recommended, especially when the number of independent variables is high. Other variations of regression are available for research questions that involve the prediction of nominal or ordinal level dependent variables or for the prediction of more than one dependent variable. Interpretation The preceding discussion focused on the mechanics of statistical analysis. This section focuses more on the fuzzier aspects of statistical analysis, statistical reporting, and interpretation. Although limited somewhat by journal requirements, authors have some leeway in how they present the results of their statistical analysis. The reader is advised to examine several journals for statistical output formats to find one that is most appealing and understandable (unless restricted by journal). The author also may elect to place results only within the text of the manuscript. Although tables often are easier to read, simple results may not merit the space required for a table or figure. When reporting findings in text, the author must report the names of the independent and dependent variables, the statistical procedure used, the statistic calculated (eg or F), the value of the statistic, appropriate degrees of freedom (df, from the computer printout), and the P value (in relation to alpha or actual value). For example, a narrative report of the t-test described earlier would be that the mean flight time for nontrauma patients was statistically higher than for trauma patients, t = 2.82, df = 644, P < .05. More commonly, the exact P-value provided in the computer printout is reported (eg, P = .0049). A final topic for discussion is the interpretation of statistical output. Knowledge of more than the numbers obtained from the computer is needed to adequately interpret a statistical analysis. The first consideration is clinical versus statistical significance. Not all results that achieve statistical significance have clinical significance. For example, in a study of the effect of drug A on chronic hypertension, a statistically significant difference between the control and experimental group was found. However, the average systolic blood pressure for the control group was 162, and the average systolic blood pressure for the experimental group was 158. Although this drop in blood pressure was statistically significant, such a small drop in blood pressure has no clinical significance to the patient. In addition, a larger drop still might be considered clinically insignificant if the cost of the drug was extreme in comparison with a moderate drop in blood pressure. Statistically significant results that do not have clinical significance are more common in studies with large sample sizes. For this reason, investigators often decrease alpha in the case of a large sample so as to diminish this problem (and decrease the chance of a type I error). On a very rare occasion, a study might have clinically significant findings without achieving statistical significance. This most often occurs in the event of small sample sizes. For example, a pilot study of 10 patients—five in the control and five in the experimental group—might show a large difference in the means of the systolic blood pressure for the two groups. However, large variability in systolic blood pressure between subjects in each group (large standard deviation) and a small sample size may preclude finding statistical significance. The researcher still may wish to consider the results clinically significant and worthy of further investigation. A final caution about the interpretation of statistical analyses is that statistics can be misleading. A reader should look to a report of the study to find evidence that many of the decisions that are important to statistical analysis are made before the study, not after the results are obtained. For example, an investigator should not wait until after obtaining the P-value before selecting an alpha. Other methods for manipulating an analysis of which the reader should be aware include subdividing or combining groups within the sample to maximize the results. At times there are good reasons to alter group designations (too few subjects per cell), but these possibilities should be discussed before the analysis and not after. Finally, statistical significance can be found if the investigator looks hard enough. As discussed previously, the P-value relates to the probability of finding results only by chance. If an alpha is set at .05, and 20 analyses are computed, at least one of the analyses is likely to be statistically significant merely by chance (5% chance). Because research journals often are more interested in studies that demonstrate statistical significance, an author may be tempted to do multiple exploratory analyses merely to find something that is publishable. Here, being able to link the review of literature to the research questions and results becomes very important. Such links help to demonstrate that the questions were well thought out in advance and not merely the result of creative data analysis. Conclusion We would like to stress that conducting and interpreting a statistical analysis are not impossible. Understanding a few basic concepts will provide a background helpful for interpreting a wide range of statistics, even if the details of the math are not understood. The researcher also is reminded to seek out the assistance of a statistician early in the process of research. A statistician can provide direction, education, and reassurance as you grow in your statistical sophistication. References 1 CB Thompson Basics of research part 13: Descriptive data analysis Air Med J, 58 (2009), pp. 56-59 ArticleDownload PDFView Record in ScopusGoogle Scholar 2 EA Panacek, CB Thompson Basics of research part 5: Sampling methods: Selecting your subjects Air Med J, 26 (2007), pp. 75-78 ArticleDownload PDFView Record in ScopusGoogle Scholar 3 S Allua, CB Thompson Basics of research part 14: Hypothesis testing Air Med J (2009) Google Scholar 4 GM Diekhoff Basic statistics for the social and behavioral sciences, Prentice Hall, Upper Saddle River, NJ (1996) Google Scholar 1 Shane Allua, PhD, is a senior consultant for a global business consulting company located in Bethesda, MD. 2 Cheryl Bagley Thompson, PhD, RN, is an associate professor and assistant dean of informatics and learning technologies at the University of Nebraska Medical Center College of Nursing in Omaha. View Abstract Copyright © 2009 Air Medical Journal Associates. Published by Mosby, Inc. All rights reserved. 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By continuing you agree to the use of cookies. Copyright © 2021 Elsevier B.V. or its licensors or contributors. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. • Implementation Science: Why It Matters for the Future of Social Work Leopoldo J. Cabassa ABSTRACT Bridging the gap between research and practice is a critical frontier for the future of social work. Integrating implementation science into social work can advance our profession’s effort to bring research and practice closer together. Implementation science examines the factors, processes, and strategies that influence the uptake, use, and sustainability of empirically supported interventions, practice innovations, and social policies in routine practice settings. The aims of this article are to describe the key characteristics of implementation science, illustrate how implementation science matters to social work by describing several contributions this field can make to reducing racial and ethnic disparities in mental health care, and outline a training agenda to help integrate implementation science in graduate-level social work programs. ARTICLE HISTORY Accepted: December 2015 Social work leaders (Brekke, Ell, & Palinkas, 2007; Proctor et al., 2009; Rubin, 2015; Thyer, 2015) and national reports from the Institute of Medicine (2001, 2003), the U.S. Department of Health and Human Services (2001), and the National Institute of Mental Health (2015) have noted a growing chasm between the knowledge generated from our best clinical and services research and the integration of this evidence in routine practice settings. This means that social workers in the community often lag behind the best available science and knowledge base that should be informing their practices, and that researchers lag behind understanding critical services needs and questions relevant to social work practice that should be informing their studies. Bridging the gap between research and practice is a critical frontier for the future of social work. Past approaches such as the empirical clinical-practice movement, the increase in empirically supported treatments, and the evidence-based practice model, have fallen short in narrowing the gap between social work research and practice (Thyer, 2015). These approaches have advanced the evidence base of social work practice but have tended to rely on “a unidirectional flow from research to practice” without a clear understanding of how the context and realities of practice shape the use of research in practice settings and how the generation of practice-based evidence can help integrate research and practice (Epstein, 2015, p. 499). Implementation science can advance social work’s effort to bring research and practice closer together because this emerging field focuses on understanding the processes and factors that influence the integration and use of research and empiricallysupported interventions and policies into practice across multiple service sectors relevant to social work (e.g., health and mental health care systems, child welfare, schools, social services; Proctor et al., 2009). The aims of this article are to (a) describe the key characteristics of implementation science, (b) illustrate how implementation science matters to social work by presenting several contributions this field can make to reducing racial or ethnic disparities in mental health care, and (c) outline a training agenda to integrate implementation science in graduate-level social work programs. CONTACT Leopoldo J. Cabassa ljc2139@columbia.edu Columbia University School of Social Work, 1255 Amsterdam Avenue, New York, NY 10027. This is an invited article. © 2016 Council on Social Work Education JOURNAL OF SOCIAL WORK EDUCATION 2016, VOL. 52, NO. S1, S38–S50 http://dx.doi.org/10.1080/10437797.2016.1174648 What is implementation science? Implementation science is the scientific study of methods that examine the factors, processes, and strategies at multiple levels (e.g., clients, providers, organizations, communities) of a system of care that influence the uptake, use, and ultimately the sustainability of empirically-supported interventions, services, and policies into practice in community settings (Palinkas & Soydan, 2012; Proctor et al., 2009). It is commonly considered one of the last stages of the intervention research process that follows the results of effectiveness studies (Brekke et al., 2007; Fraser, 2004). At this stage, implementation focuses on taking interventions that have been tested using methodologically rigorous designs (e.g., randomized controlled trials, quasi-experimental designs) under real-world conditions and found to be effective and integrating the results of these studies into practice using deliberate strategies (Powell et al., 2012; Proctor et al., 2009). Social work intervention research and implementation science are applied disciplines but differ in fundamental ways (see Table 1). Social work intervention research examines the development, efficacy, and effectiveness of specified interventions, whereas implementation science examines how to move and adopt these effective interventions into practice. The impetus of intervention research is to test whether a specified intervention usually applied to individuals, families, groups, providers, and sometimes communities compared to another intervention, no intervention at all, or the status quo, achieves desirable outcomes that focus primarily on improving health, social, and mental health indicators, functioning, quality of life, satisfaction with services, and quality of care, among others. Implementation science also uses specified intervention or strategies, but these tend to be applied to providers, organizations, and even systems of care, to achieve desirable outcomes that focus on improving the uptake and use (e.g., acceptability, feasibility, fidelity, sustainability) of the intervention in a specific practice setting. Three fundamental characteristics encapsulate implementation science. First, the implementation of empirically-supported interventions or practice innovations is a dynamic social process that is shaped by the context or ecology in which the practice innovation takes place and the people Table 1. Characteristics of social work intervention research and implementation science. Social Work Intervention Research Implementation Science Key research aim Develop and test the efficacy and effectiveness of social work interventions. Understand the factors, processes, and strategies that shape the uptake, use, integration, and sustainability of social work interventions and practice innovations in practice Example of research questions Does the intervention work under ideal conditions? (efficacy) What factors facilitate or hinder the widespread use of an empirically-supported intervention in specific practice settings? Does the intervention work under realworld conditions? (effectiveness) What strategies can administrators, managers, and clinicians use to increase the use of an empirically-supported What impact does the intervention intervention in a specific practice setting? have on individual- and family-level outcomes? Common study designs Quasiexperimental trials Observational studies Randomized controlled trials Mixed-methods designs Hybrid effectiveness/implementation designs Common units of analysis Clients Providers Providers Organizations Systems of care Examples of change strategies or interventions Cognitive behavioral therapy Learning collaboratives Motivational interviewing Train the trainer Antipoverty program Availability responsiveness and continuity intervention Common outcomes Health and mental health indicators Adoption Social indicators Acceptability Functioning Appropriateness Quality of life Cost Quality of care Feasibility Satisfaction Sustainability Fidelity JOURNAL OF SOCIAL WORK EDUCATION S39 involved in this process (Damschroder et al., 2009). As stipulated by Everett Rogers (1995) in his influential diffusion of innovation theory, an “innovation almost never fits perfectly in the organization in which it is being embedded” (p. 395). This suggests that implementation can be characterized as a mutual adaptation process in which the practice innovation (e.g., empirically-supported interventions, social policies) being implemented and the organizations and stakeholders (e.g., providers, administrators) involved in the implementation process must adjust to the new parameters of the innovation and the exchange of knowledge, attitudes, social norms, and practices that occur throughout this complex process (Damschroder et al., 2009; Palinkas & Soydan, 2012). Implementation is a social process that unfolds over time, transforming the ecology of practice to enhance the fit, use, and eventually the integration of a practice innovation in organizations or systems of care (Cabassa & Baumann, 2013). Second, implementation requires the interaction, collaboration, and participation of stakeholders at multiple levels of an organization or system of care (Aarons, Horowitz, et al., 2012). Organizational leaders, directors, managers, administrators, service providers, frontline staff, clients, and their family members are all directly or indirectly involved, as implementation entails a multitude of social processes, including planning, decision making, negotiating, prioritizing, problem solving, service delivery, restructuring, and the allocation of resources. The more complex the practice innovation being implemented, the more social interactions and involvement of stakeholders is needed. The participation and engagement of stakeholders is a critical ingredient of the implementation process as moving interventions into practice requires knowledge and expertise about the intervention and locally grounded knowledge, skills, and understanding about the settings and communities in which the intervention will be used. Implementation science is thus a collaborative endeavor. Third, implementation is inherently a change process (Weisz, Ng, & Bearman, 2014). It entails the introduction, use, and integration of a new way of doing things within an organization or system of care. Implementation is a change in the status quo that requires alterations, modifications, adaptations, and adjustments in attitudes, social norms, practices, procedures, behaviors, and even policies. At the heart of this change process is the use of implementation strategies that are systematic processes and practices intended to facilitate the adoption of a specified practice innovation into usual care to address gaps in services or in quality of care (Powell et al., 2012). In all, implementation science can help social work develop sustainable, bidirectional bridges between research and practice to increase the relevance, use, impact, and sustainability of the best available evidence from clinical and services studies to improve the access, quality, and outcomes of social work interventions, services, and social policies. How implementation science matters: A case study in reducing disparities in mental health care Implementation science matters for the future of social work because it can help address many of the grand challenges facing our profession (American Academy of Social Work and Social Welfare, 2013). One such challenge where implementation science can make a significant difference is in the reduction of racial and ethnic disparities in mental health care in the United States. Social workers are at the front lines for combating these inequities in mental health care as our profession delivers the majority of mental health care in the United States (Proctor, 2004). In this section, I use examples from my own work and the work of others to illustrate how implementation science can help address racial and ethnic inequities in mental health care and help move this important area of social work forward. This discussion is not a systematic literature review but is meant to serve as a case study describing several contributions implementation science can make to the field of mental health care disparities. In the Institute of Medicine (2003) report Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, inequities in care were defined as differences in health care treatments received by different groups (e.g., racial or ethnic minorities vs. non-Latino Whites) that cannot be accounted for by differences in the health care needs or preferences of these groups and are affected S40 L. J. CABASSA by the operations and ecology of the health care system, legal and regulatory climate, and discrimination and biases. In the area of mental health care, it is well established that racial and ethnic minorities in the United States face persistent inequities along the entire continuum of mental health care. Compared to non-Latino Whites, racial and ethnic minorities are more likely to underuse mental health services, discontinue treatments prematurely, and receive mental health care that is poor in quality even after adjusting for differences in educational levels, health insurance rates, and mental health needs (Institute of Medicine, 2003; U.S. Department of Health and Human Services, 2001). These mental health care disparities also contribute to greater persistence, severity, and burden of mental disorders among racial and ethnic minority communities (Alegria et al., 2008; Williams et al., 2007). Social workers have ethical and professional obligations to eliminate racial and ethnic disparities in mental health care. As described in the Preamble to the National Association of Social Workers (NASW; 2008) Code of Ethics, social workers “seek to promote the responsiveness of organizations, communities, and other social institutions to individuals’ needs and social problems” particularly among historically underserved populations (preamble, para. 2). Disparities in mental health care arise and are perpetuated because the providers, organizations, communities, and social institutions responsible for delivering mental health care fail to meet the needs of these vulnerable populations because of a constellation of factors (e.g., cost, lack of culturally sensitive services, stigma, fragmentation of care, dearth of bilingual providers). In the following sections, I describe how implementation science can help reduce inequities in mental health care for these historically underserved communities by facilitating the implementation of empirically-supported interventions known to reduce disparities in care, designing and selecting interventions with implementation in mind, and blending the cultural adaptations of interventions with implementation science (see Table 2 for a summary of these areas). Table 2. Examples of how implementation science can help address racial and ethnic disparities in mental health care. Implementation Science Contributions Summary of Key Points Examples of Implementation Science Articles Examples of Research Questions Relevant to Implementation Science Implement what works. Implement empirically-supported interventions shown to improve access, quality, and quality of care in minority communities. Use implementation strategies to move empiricallysupported interventions into minority communities. Wells et al. (2013) used a grouplevel randomized comparative effectiveness trial design to test the impact of two implementation strategies to translate a quality improvement program for depression care in minority communities. Which type of implementation strategies (single, multifaceted, blended), produce the best implementation and clientlevel outcomes to reduce disparities in mental health care? Design and select interventions with implementation in mind. Consider the ecology of practice in which the intervention will be used to inform the intervention development process. Engage stakeholders from the very beginning using CBPR approaches to inform the selection and design of interventions. Cabassa et al. (2013) used photovoice in two supportive housing agencies to engage diverse clients with SMI to inform the selection and design of a health intervention. What methods or approaches can be used to involve stakeholders in the process of intervention development? How do CBPR approaches facilitate the development of interventions that are community informed and sustainable? Blend cultural adaptations of interventions with implementation science. Blend implementation science and cultural adaptation of mental health treatments to create better avenues for translating the best available mental health treatments into routine practice in minority communities. Cabassa et al. (2014) used the collaborative intervention planning framework to adapt an existing health care manager intervention to a new patient population (Latinos with serious mental illness) and provider group (social workers) to increase its fit with the local practice setting. What elements of an existing intervention or context of practice need to be adapted to enhance cultural relevance and social validity? Source. Note. CBPR=community-based participatory research; SMI=serious mental illness. JOURNAL OF SOCIAL WORK EDUCATION S41 Implement what we know works in racial and ethnic minority communities A growing literature supports the effectiveness of several empirically supported interventions (e.g., depression treatments for adults, attention deficit hyperactivity disorder care for children, parent management training) for reducing mental health care disparities, particularly for African Americans and Latinos (Miranda et al., 2005). Yet, these interventions are rarely implemented in community settings serving minority populations. Implementation science can help address this important gap by using implementation strategies to put these empirically supported interventions into practice. Implementation strategies are systematic and planned processes and actions that are designed to help move and integrate empirically-supported interventions into specific practice settings (Powell et al., 2012). As described by Powell et al., implementation strategies can take many forms, such as discrete single actions (e.g., training workshops), multifaceted approaches that combine discrete actions (e.g., training workshops with supervision and fidelity feedback), or blended methods that incorporate a variety of actions into a specified package (e.g., learning collaboratives). Implementation strategies are used “to plan, educate, finance, restructure, manage quality, and attend to the policy context to facilitate implementation” (Powell, Proctor, & Glass, 2014, p. 193). Primary care is one setting in which using implementation strategies to move empirically supported interventions can have profound impacts in reducing disparities in mental health care. Primary care clinics are a common site for racial and ethnic minorities to turn to for mental health care, particularly for depression (Cabassa & Hansen, 2007; U.S. Department of Health and Human Services, 2001). Quality improvement programs for depression in primary care that use a collaborative-care approach produce better depression outcomes than the usual care for African Americans and Latinos (Cabassa & Hansen, 2007; Miranda et al., 2003). Despite these important results, racial and ethnic disparities in depression care still persist. Linking specific implementation strategies with effective depression interventions can address disparities in depression care as shown in a group-level randomized comparative effectiveness trial conducted in racial and ethnic minority communities in Los Angeles (Wells et al., 2013). Ninetythree matched programs from health, social, and other service sectors were randomly assigned to one of two different implementation strategies to translate a quality improvement program for depression care. The first strategy, named resources for services (RS), offered technical assistance to community programs using a train-the-trainer paradigm that employed webinars plus site visits to train programs on the depression care program. The trainers for this strategy included a nurse care manager, licensed psychologist, three board-certified psychiatrists, support staff, and a community service administrator to support participation and cultural competence. The second strategy, called community engagement and planning (CEP), invited agency administrators to biweekly meetings for 5 months to build training capacity for delivering the intervention and networks to support services. The planning for the CEP strategy was co-led by community and academic partners and followed the principles of community-partnered participatory research, a form of community-based participatory research (CBPR) that promotes two-way knowledge exchange, trust, and capacity building (Jones & Wells, 2007). The CEP condition also used a workbook for developing implementation plans tailored to the community and for monitoring the implementation process to make course corrections as needed. This study found that the CEP strategy was more effective than the RS strategy at improving mental-health-related quality of life, increasing physical activity, and reducing risk factors for homelessness. CEP also shifted clients’ use of services for depression by reducing hospitalizations and specialty medication visits and increasing visits to primary care and other community-based sectors of care (e.g., faith-based programs) (Wells et al., 2013) . These findings indicate that CEP is a viable implementation strategy that can be used in racial and ethnic minority communities for moving effective depression care programs into routine practice. This type of implementation study moves the field of mental health care disparities research and practice forward as it goes beyond testing the effectiveness of interventions and produces the necessary evidence to identify which S42 L. J. CABASSA implementation strategy works best for improving depression care in historically underserved communities. More studies linking mental health care disparities research and implementation science are needed to advance the knowledge base on how to best implement what we know works in racial and ethnic minority communities. Design and select interventions with implementation in mind Although several empirically supported interventions exist for addressing mental health care disparities, gaps in the knowledge base continue to exist. Many mental health interventions are not developed and rigorously tested in racial and ethnic minority communities (Aisenberg, 2008). In a review of 75 randomized controlled trials conducted between 2001 to 2010 across several mental health conditions (e.g., bipolar disorder, schizophrenia, major depression) that included a total of 14,646 participants, racial and ethnic minorities were seriously underrepresented, accounting for 19% of the total sample in these trials (Santiago & Miranda, 2014). Asian Americans/Pacific Islanders represented 1%, and American Indians/Alaska Natives were less than 0.01% of the total sample. This stark underrepresentation raises serious concerns about the validity of the evidence base of mental health interventions for racial and ethnic minority groups, and points toward the need to reconfigure the process of intervention development for these historically underserved communities. Implementation science can help address this need by informing the process of intervention development. This approach considers from the early stages of intervention development the typical circumstances in which the intervention will be used so that what is developed fits with the ecology of practice. Examples of implementation issues that can inform the selection and development of interventions include client characteristics (e.g., health and mental health comorbidities, cultural factors, language proficiencies, income, competing social and economic demands, educational levels, etc.), provider factors (e.g., training, supervision, biases and discrimination, competing tasks and responsibilities, professional roles, attitudes toward evidence-based practice), organizational features (e.g., resources, policies, reimbursement regulations, organizational culture and climate, funding streams, leadership, institutional racism), and community-level factors (e.g., cultural norms toward mental illness and mental health treatments, stigma, community resources and assets, policies and political interests). Moreover, attention to implementation outcomes, such as feasibility, acceptability, appropriateness, cost, and sustainability, can also be considered in the early stages of intervention development (Proctor et al., 2011). Designing and selecting interventions with implementation in mind can be accomplished by forming partnerships with stakeholders (e.g., clients, community members, providers, researchers) from the very beginning of this process. CBPR is one approach used in translational research that focuses on fostering synergistic collaborations between stakeholders by capitalizing on their shared knowledge, wisdom, and expertise (Cabassa et al., 2013). CBPR contributes to implementation science by (a) helping contextualize interventions to the realities and conditions of specific communities and settings; (b) integrating social and cultural values, perspectives, and norms into the development and implementation of interventions to enhance their relevance, acceptability, and effectiveness; and (c) strengthening the capacities of stakeholders to produce community-engaged research and practices critical for reducing inequities in health (Jones & Wells, 2007; Wallerstein & Duran, 2010). Our group published an article describing how we used photovoice, a CBPR approach, in partnership with two supportive housing agencies in New York City to inform the selection and design of an intervention aimed at improving the physical health of Latinos and African Americans with serious mental illness (SMI), for example, schizophrenia and bipolar disorder (Cabassa et al., 2013). Photovoice is a participatory research method that empowers participants to use photographs, narratives, and dialogue to communicate and critically reflect on their shared experiences and inform social action (Minkler & Wallerstein, 2008). In this study, we conducted two photovoice groups, one at each agency. Each group met for six consecutive weeks and consisted of eight JOURNAL OF SOCIAL WORK EDUCATION S43 participants, mostly African Americans and Latinos recovering from SMI. In these groups, participants discussed the photographs they took in their communities related to their physical health and wellness. The results of this study showed how using photovoice can generate valuable information about clients’ preferences for the format, content, and methods of a health intervention. Participants in the study indicated they would prefer an intervention delivered by peer specialists rather than professionals (format), that is focused on weight loss and physical activity (content), and uses experiential approaches (e.g., cooking demonstrations) to help clients develop the necessary skills to live a healthy lifestyle (method). This study illustrated how participatory research methods “can foster community engagement and social action among vulnerable and often overlooked populations by providing the space and tools for community members to actively contribute to the generation of knowledge and wisdom essential” for designing and selecting interventions that are grounded on the realities of the community (Cabassa et al., 2013, p. 628). Using implementation science to inform the design of interventions in racial and ethnic minority communities requires community engagement that bridges research and practice and values multiple forms of knowledge. Designing and selecting interventions with implementation in mind is an approach that intends to reconfigure the process of intervention development by examining from the very beginning how the context of practice influences the use of the interventions in community settings to enhance their relevance, acceptability, cultural sensitivity and sustainability. The ultimate goal of this approach is to help accelerate the development and testing of empirically-supported interventions and practice innovations that can be implemented in the community to reduce inequities in mental health care. Blend cultural adaptations of interventions with implementation science Culture shapes many aspects of mental health care, including help-seeking decisions, pathways to care, the expression and identification of mental disorders and psychological distress, engagement and retention in mental health treatments, and the delivery of mental health care (Kirmayer, 2012; U.S. Department of Health and Human Services, 2001). The basic assumption of adapting existing mental health interventions to clients’ culture is that “by explicitly integrating cultural factors (e.g., language, cultural values, gender roles) into care, the relevance, acceptability, effectiveness, and sustainability of treatments will be increased, and inequities in care will be narrowed” (Cabassa & Baumann, 2013, p. 2). Meta-analyses have found that culturally adapted, empiricallysupported interventions can produce small to moderate treatment benefits when compared to different conditions, for example, placebo, treatment as usual, waitlist conditions, or nonadapted interventions (Benish, Quintana, & Wampold, 2011; Griner & Smith, 2006; Huey & Polo, 2008; Smith, Domenech Rodriguez, & Bernal, 2011). These benefits seem to be linked to adaptations that target treatment goals, clients’ explanatory models of illness, and the incorporation of metaphors that match clients’ cultural views to intervention materials (Benish et al., 2011; Griner & Smith, 2006). Culturally adapted interventions seem to work best for certain groups, such as low-acculturated Latinos, non-English speaking clients, older clients, and when the intervention is delivered to a racially or ethnically homogenous group (Griner & Smith, 2006). Despite these results, culturally adapted mental health interventions remain largely unused in racial and ethnic minority communities. Cabassa and Baumann (2013) described three critical areas for integrating the fields of cultural adaptation of mental health interventions and implementation science to create better avenues for translating the best available mental health treatments into practice. First, the explicit use of existing cultural adaptation models in the implementation process can help clarify how cultural factors at the client or provider levels have an impact on the use and outcomes of mental health interventions. Common features of these models include collaborations between treatment developers and stakeholders, use of formative research methods (e.g., focus groups) to understand the context of practice S44 L. J. CABASSA and clients’ needs and strengths, consideration of provider factors (e.g., skills, training, cultural competence) to enhance the ecological validity of the intervention, use of iterative pilot testing to refine intervention adaptations, and use of rigorous designs to test the effectiveness of the adapted intervention (FerrerWreder, Sundell, & Mansoory, 2012). Second, blending the principles and methods used in these two fields can help specify and document what aspects of the intervention or the context of practice needs adaptations, at what levels (e.g., clients, providers, organization), and how these adaptations, if necessary, affect clientlevel and implementation outcomes. Third, applying the ecological lens commonly employed in implementation science to the adaptation process can help assess and identify contextual factors at multiple levels that influence the use and integration of interventions in community settings. Studies examining the relationships between contextual factors and the adoption of practice innovations indicate that these distal factors play an important role in the implementation process (Aarons, Horowitz et al., 2012). For example, organizational factors such as the size of organizations, the division of units and departments within organizations, a decentralized decisionmaking structure, and leadership support and champions have been found to facilitate the implementation process (Greenhalg, Glenn, MacFarlane, Bate, & Kyriakidou, 2004). The collaborative intervention planning framework provides an example of how to blend cultural adaptations methods and implementation science (Cabassa, Druss, Wang, & Lewis-Fernandez, 2011). This framework combines CBPR and intervention mapping (IM) to inform the intervention adaptation process. CBPR principles (e.g., mutual trust, capacity building) are used to develop and foster a partnership between researchers and stakeholders involved in the delivery of the intervention through the formation of a community advisory board (CAB). IM, a systematic approach that uses group activities (e.g., brainstorming exercises) and visual tools (e.g., logic models) to develop a road map for the development, adaptation, and implementation of interventions (Bartholomew, Parcel, & Kok, 2006), is then used to put the CAB partnership into action. The collaborative intervention planning framework provides a set of steps, procedures, and methods drawn from cultural adaptation models and implementation science that enable stakeholders to systematically analyze the fit of each intervention component to the client population, provider groups, and local practice setting. We applied this framework to adapt an existing health care manager intervention to a new client population (Latinos with SMI) and provider group (social workers) to fit the context of a public outpatient mental health clinic in New York City (Cabassa et al., 2014). The adaptation process included fostering collaborations between CAB members; understanding the needs of the local population through a mixed-methods needs assessment, literature reviews, and group discussions; critically examining intervention objectives to identify targets for adaptation; and developing the adapted intervention. The application of the collaborative intervention planning framework helped identify a series of cultural- and provider-level adaptations that enhanced the relevance, acceptability, feasibility, and cultural sensitivity of the health care manager intervention without compromising its core components. Overall, blending the cultural adaptations of mental health interventions with implementation science can create better avenues for translating the best available mental health treatments into routine practice in minority communities (Cabassa & Baumann, 2013). Implementation science training agenda for graduate-level social work programs In this section, I outline the beginning components of a training agenda that could be used to integrate implementation science in master’s-level social work programs. The learning objectives for this training agenda include (a) identifying and analyzing gaps between research and practice in different practice settings and populations; (b) critically examining and using different implementation science theories and frameworks to understand and address gaps in mental health care; (c) applying implementation science methods to understand the processes, factors, and practices that influence the integration of research and practice in different practice settings and populations; (d) using different implementation strategies to facilitate the use of empirically-supported interventions JOURNAL OF SOCIAL WORK EDUCATION S45 and practice innovations; and (e) communicating to policy makers, practitioners, and the public at large the benefits of using implementation science to improve the access, quality, outcome, and sustainability of mental health services across different settings and populations. This training agenda aims to increase students’ knowledge of the gap between social work research and practice, and provide students with the basic foundations on implementation science. This agenda includes integrating general knowledge of implementation science throughout the MSW curriculum, using implementation science to inform field education, and developing specialization programs on implementation science. Integrate general knowledge of implementation science throughout the MSW curriculum The integration of implementation science in the curriculum of MSW programs can take on many forms. Implementation studies and readings could be introduced and discussed in foundation-level courses, particularly when presenting and discussing the principles and steps of evidence-based practices In research methods and program evaluation courses, instructors can present methods commonly used in implementation science, discuss existing implementation studies in areas relevant to social work, and discuss the relevance of these methods and approaches for examining social work practice and policies. They can also encourage students to develop projects and proposals that have a focus on implementation science. In policy courses, implementation theories and frameworks could be introduced to discuss how laws, regulations, funding mechanisms, and political forces have an impact on the introduction, use, and sustainability of empirically-supported interventions in different systems of care relevant to social work. In more advanced clinical courses where students learn how to deliver empirically-supported interventions, implementation science readings, discussions, and case studies could be presented to discuss the factors and processes that influence the use of these interventions in different practice settings and populations. Assignments in these clinical courses (e.g., papers, group presentations) could be included in which students use existing implementation science theories to conduct an ecological scan identifying factors and processes at multiple levels of their field placement agencies that could facilitate or hinder the use of these empirically-supported interventions. Integrating general knowledge of implementation science throughout the MSW curriculum would provide students with the basic knowledge and skills necessary to begin understanding implementation issues in their fields of practice. Use implementation science to inform field education Field education is one of the greatest yet underdeveloped assets that the social work field has for creating bridges between research and practice. Implementation science could be used to inform students’ field education experiences to gain a deeper understanding of the gaps between research and practice, and provide real-world experiences to prepare them to address these gaps as they move into the workforce. At a foundational level, field placements could be structured to help students gain a deeper appreciation of the ecology of practice that affects the integration of social work research and practice. Field placements could be organized for students to systematically rotate through various organizational roles (e.g., quality assurance staff, administration) that go beyond providing clinical assessment and treatments in an agency to gain a deeper understanding of the dayto-day operations of an agency and the context of practice (Weisz et al., 2014). Field placement sites could be developed in organizations that focus on implementing and scaling up empirically-supported interventions in systems of care. A cadre of these types of organizations currently exists in some state and city governments and in the Veterans Administration. For instance, the New York State Office of Mental Health in 2007 established the Center for Practice Innovations (CPI) to support the implementation of empirically-supported mental health interventions throughout New York state. CPI uses state-of-the art implementation approaches (e.g., learning S46 L. J. CABASSA collaboratives) to scale up practice innovations (e.g., assertive community treatment, supported employment and education, treatment of first-episode psychosis), enhance and maintain practitioners’ expertise, build stakeholder collaborations, and develop agencies’ infrastructure to support the adoption and sustainability of empirically supported interventions (Covell et al., 2014). A field placement at an organization such as CPI would provide MSW students with rich practical experiences in the application of implementation science in real-world settings. Field placement opportunities could also be integrated into implementation studies conducted by social work faculty members. For example, at the Columbia University School of Social Work, where I teach and conduct research, I established a field placement site with the help of our field education department for students in our advanced clinical social work practice and advanced generalist practice and programming concentrations as part of a study funded by the National Institute of Mental Health (Cabassa et al., 2011). As part of this field placement, students were assigned to a public outpatient mental health clinic in New York City, the community partner for our study. At this clinic, students received clinical training and experiences working with adult clients with serious mental illness (e.g., schizophrenia, bipolar disorder) from licensed clinical social workers and participated in a variety of implementation science activities, including discussing directed readings in implementation science, participating in a CAB charged with adapting and implementing a health care manager program for Latino clients with SMI and at risk for cardiovascular disease, helping in the analysis and interpretation of stakeholder (e.g., administrators, clinicians, peer advocates) interviews that informed intervention adaptations and implementation, and delivering the adapted health care manager program to a small group of clients under the supervision of our research staff and clinicians from their field placement. Using implementation science to inform field education can provide a useful training platform for students to learn about the application and practice of implementation science in community agencies. Offer specialization programs or certificates on implementation science This approach enables social work students to develop a set of specialized knowledge and skills in implementation science. These specialty programs could combine classroom learning, online courses or workshops, and field placement opportunities. They could also include courses in other disciplines relevant to implementation science (e.g., organizational psychology, management and administration, public health). Instructors for these programs should include existing social work faculty engaged in implementation studies as well as practitioners from multiple fields of practice with realworld expertise who are directing implementation efforts at their organizations. These programs require the development of a package of courses and training opportunities that focus on the theories, research methods, and practices necessary to prepare social work students to practice implementation science. Some of these courses already exist in some social work schools, such as courses in CBPR, quality monitoring and improvement in the social services (see http://www.qualitysocialservice.com/ for a description and materials for this course), and implementing and evaluating evidence-based practice. Other courses relevant to implementation science would need to be developed (e.g., research and evaluation methods for implementation practice, introduction to the development and application of implementation strategies). This specialization in implementation science could cut across different fields of social work practice or be located in specific social work concentrations (e.g., health and mental health, gerontology, child welfare). Given the applied nature of implementation science, field education should be integrated into these specialized programs for students to apply the knowledge and skills they learn in their specialization courses. Programs could also require students to complete a master’s thesis or an applied project that focuses on a relevant implementation science topic. Programs could entail conducting practice-based research at a field placement site applying a variety of research designs relevant to implementation science (e.g., observational studies, quasi-experimental designs, mixed JOURNAL OF SOCIAL WORK EDUCATION S47 methodologies, participatory research designs) and focus on exploring, describing, and testing how different processes and factors promote the use of empirically-supported intervention, practice innovations, or social policies in routine practice settings to address a gap in care. These specialized programs aim to develop the next generation of social work professionals who have specialized knowledge and the expertise necessary to direct implementation efforts in different areas of social work practice and contribute to the development of a science and practice of implementation in the social work profession. Conclusion Bridging the gap between social work research and practice has been a long-standing problem in our profession (Thyer, 2015). In this article, I discussed how implementation science can serve as a bidirectional bridge to advance our profession’s efforts to bring research and practice closer together. From the research side, implementation science is an applied discipline that provides a variety of theories, frameworks, and methods to understand the factors and processes that influence the uptake, use, and sustainability of empirically-supported interventions, practice innovations, and social policies in practice. This research is critical for understanding how the ecology of practice influences the integration of our best available evidence from clinical and services studies into realworld practice settings. From the practice side, implementation science provides practitioners with the skills, tools, and knowledge base to identify and analyze gaps in services and quality of care, and use practical strategies to facilitate the integration of interventions, programs, or policies into practice. In sum, integrating implementation science into social work can help advance our profession’s most basic mandate “to enhance human wellbeing and help meet the basic human needs of all people” (NASW, 2008, preamble, para 2.) by putting into practice what we know works from our most rigorous social work interventions and services research; helping develop, adapt, and use interventions and practice innovations that fit the conditions of practice and meet the needs of our clients; and preparing our workforce to take leadership positions in implementation efforts. Implementation science matters for the future of social work because it can help our profession develop bidirectional bridges between research and practice to increase the relevance, use, impact, and sustainability of our best available interventions, services, and social policies. Funding This work was supported in part by National Institutes of Health grants K01 MH091108 and R01 MH104574. 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Search in: Advanced search Journal of Poetry Therapy The Interdisciplinary Journal of Practice, Theory, Research and Education Volume 26, 2013 - Issue 2 Journal homepage 1,907 Views 30 CrossRef citations to date Altmetric ListenReadSpeaker webReader: ListenFocus Articles Poetic inquiry and multidisciplinary qualitative research Debbie McCulliss Pages 83-114 Published online: 07 May 2013 • Download citation • https://doi-org.proxy1.ncu.edu/10.1080/08893675.2013.794536 In this article • Introduction • Comparison of qualitative and quantitative research • Methodologies for qualitative data collection • Major approaches to qualitative research • Poetic inquiry • Exploration of poetic characteristics • Approaches and uses of poetry for qualitative research • Conclusion/summary • References • Full Article • Figures & data • References • Citations • • Metrics • Reprints & Permissions • PDF Abstract The focus of this article is on the use of poetry as a medium for qualitative research. The methodologies for qualitative data collection and positions of the role of poetry as a tool within disparate disciplines such as anthropology, sociology, business, and medicine are reviewed. The ways in which poetry may be useful to provide insight and critical perspective to research are examined. Examples of studies using poetry as a catalyst or evaluative tool are provided. The advantages and disadvantages of using poetry in qualitative research are noted. Keywords: Anthropologyarts-based researchethnographymedicinenursingpoetrypoetic inquiryqualitative researchsociology Introduction The voice of the poet is needed in our prose-flattened world. (Brueggemann, 2003, pp. 1–11) The use of poetic inquiry in qualitative research has grown over the last several decades, especially in the social sciences. Poetry, perhaps more than any other approach or discipline, gets to the essence of qualitative methodology. It presents, and is a catalyst for, a window into the heart of human experience. Poems written by participants or study subjects serve as expressive synecdoches for their representative cultures. Poems employed as catalysts provoke insightful responses, allowing for a more in-depth and holistic understanding of the ethnography and perceptions of a particular group or population. This article sets forth the idea that poetic inquiry can be a beneficial tool for use in qualitative research. The purpose of this article is to explore the application of poetry as a tool or medium for qualitative research. Poetic inquiry is a term that describes the use of both poetic and creative thinking to analyze and draw conclusions in research, as well as a way of understanding and communicating the subject matter being studied. Poetic inquiry can give a voice back to scientific research that can easily get lost through the application of traditional scientific analysis, as the richness and experience of the people being studied is reduced to numerical data. We can, through the use of qualitative research and poetic inquiry, begin to connect empathically and understand more complex psychosocial processes (Furman, 2006b). Poetic techniques can provide researchers with the ability to honor the whole person by providing representation preserving the person's essence. The use of poetry in qualitative research can help us develop a more holistic approach to creative arts, business, education, medical, and social science research and can help broaden our understanding of how human motivation and experience influence the many aspects of life. It is this creative exploration of human nature and this gathering and interpretation of poetic responses to a situation or environment that this article seeks to discuss. To better understand the use of poetic inquiry in qualitative research using various methodologies within disparate contexts, the following areas will be described. • Comparison of qualitative and quantitative research; • Review of the prevalent methodologies for qualitative data collection; • Major approaches to qualitative research including ethnographical, expressive, foundational, hermeneutical, and traditional; • Exploration of poetic inquiry and its characteristics, showing why these characteristics are uniquely suited to qualitative research; • Discussion of the approaches and uses of poetry for qualitative research within the context of a number of fields and cultures; and • Advantages and disadvantages of using poetry as a vehicle for qualitative research. Comparison of qualitative and quantitative research There are several differences between quantitative and qualitative research. Quantitative research can tell us the facts and statistics about a subject. It can tell us if something works or not and what factors may contribute to the results. In contrast, qualitative research attempts to give us an understanding of people's thoughts and experiences. It is the “development of concepts, which helps us to understand social phenomena” (Pope & Mays, 1995, p. 42). Qualitative research can lay the foundation for understanding quantitative results and therefore complements the data derived and helps us understand the social aspect of what we are studying (Huston & Rowan, 1998). Unlike quantitative research, in which researchers distance themselves from the subjects to avoid bias in the data collection, qualitative researchers may be actively engaged with their participants. By doing so, the qualitative approach to research can help us better understand thoughts and emotions on a personal level. While quantitative research is deductive, the process of qualitative research is inductive in that the researcher builds abstractions, concepts, hypotheses, and theories from details (Merriam, 1988, p. 18). In qualitative research, the aim is a complete, detailed description of a predetermined topic, while quantitative research aims to classify features, count them, and construct statistical models in an attempt to explain what is observed. In qualitative inquiry, researchers may only have a general idea of what they are looking for before the collected data is analyzed, but in the quantitative inquiry, researchers gather data to support or refute an established hypothesis. Qualitative research takes many forms including longitudinal (research conducted over a long period), historical (collection and evaluation of data related to past occurrences), and case studies (in-depth analysis of a single person, group of people, or institution). The variations of qualitative inquiry continue to expand. At the same time, certain characteristics are associated with qualitative research. According to Gilgun (2005): ‘Qualitative’ as a term connotes flexibility of design (It involves) researchers who value and build upon reflexivity and subjectivity, constructivist assumptions, rich phenomenological descriptions, and implicit appeals to readers’ personal experiences for understanding and interpreting findings. In general, qualitative approaches are thought to be more concerned with what it means to be human— or ontological concerns—and less concerned with epistemological concerns of reliability and validity. (p. 3) The credibility of a qualitative study is based on the clarity and overall methodological rigor of the research process as well as internal validity (Côté & Turgeon, 2005, pp. 73, 74). Because of the specialized information needed, the emphasis in qualitative studies is on a smaller, more focused sample size instead of large, random samples. Qualitative analysis incorporates the categorization of data into patterns and groups as a foundation for organizing and presenting results. There are several methods qualitative researchers use to gather information including observation, participation, interviews, focus groups, and analysis of related documents and resources. Various approaches provide the researcher with many different sources of data to examine (Anderson, 2010; Prendergast, 2009b). Types of data collected may include transcripts from focus groups, in-depth or semi-structured sessions/ interviews, poems (research, data, field note, found poems), audio and video recordings,; photos and images, case studies or field notes, documents, diaries, and press clippings. Methodologies for qualitative data collection Qualitative research makes it possible to study complex phenomena in their natural context. Different excerpts will tell different stories. The qualitative researcher sifts through, organizes, and presents data in a meaningful way. The “sifting-through” process (described in different ways by proponents of different qualitative traditions) results in findings the researcher contextualizes and shapes for the audience. The researcher may, for example, emphasize the emotional tone of the findings over cognitive themes. Methods used to collect information for qualitative research include approaches that involve varying degrees of participation. External participation External participation gives the researcher the most distance from the people or situation being studied. The data is gathered by observing situations remotely. For example, some social scientists gather data about advertising from watching television and analyzing commercials. Others gather data through the use of hidden cameras or by reading journals or poetic records of people with whom they have no personal connection. Passive participation In passive participation, the researcher may be visible but does not participate or interact with others. The researchers’ role in this type of study is more observational. The researcher may, for example, visit a school and work with teachers to collect poems written by students on a particular topic. Balanced participation In balanced participation, the researcher tries to maintain a balance between observation and participation. The researcher may participate to some degree, but is not routinely active in all activities. The balanced approach may be utilized effectively in the medical or mental health fields where the researcher can interview the patient in person as well as monitor the history of the diagnosed condition through medical records and examinations. Active participation In active participation, the researcher participates as completely as possible and is fully engaged in the research. The researcher is instrumental in conducting interviews and facilitating interaction with participants. For example, the researcher may visit the participant's place of residence to interview him/her in person. The interview may be recorded or transcribed to enable further interpretation and review. Total participation Total participation refers to a study in which the researcher is also a participant. This may come about from the researcher's desire to study a subject in which he or she has personal or ongoing experience. The researcher may study psychosocial responses to an illness he or she has been diagnosed with, aspects of the experience of being a member of a biological sex, or the lived experience of a major life event. Major approaches to qualitative research Due to the wide variety of information sought in qualitative inquiry, many philosophies of research and methods of examination have been developed. These vary based on the goals, style, and training of the researcher. Some of the most common approaches are described below. Ethnographic Ethnography is the practice of attempting to discover the sociocultural aspect of a community (Patton, 2002). The strength of the ethnographic method lies in understanding the nature of certain cultural elements of a group (notions, representations, or beliefs). This understanding can evolve based on the point of view of members of the group, by observing how the group functions, or by analyzing various types of relevant documents. According to Richardson (1994): If a goal of ethnography is to retell lived experience, to make another world accessible to the reader, then the lyric poem, and particularly a sequence of lyric poems with an implied narrative, comes closer to achieving that goal than do other forms of ethnographic writing. (pp. 8, 9) Expressive research The goal of inquiry in expressive research is to expand and contextualize meanings—to value and cherish the subjective. Many studies have utilized personal, expressive poems in their qualitative research (Gallardo, Furman, & Kulkarni, 2009, p. 290). Foundational/grounded theory Foundational, or grounded, research is an inductive method of inquiry that is based on data collection from different sources. From the data, conceptual categories or theories arise. This method is often used to study a process such as a student's choice of learning strategies. Hermeneutical Hermeneutics has been used mainly as a European philosophy, developed as a tool for the interpretation of ancient religious texts. The goal of hermeneutics is to gain understanding of text (or a situation) while accepting the necessary ambiguity of translation of data without interjecting interpretation, personal prejudgments, and historical traditions. Hermeneutics argues that there is no single correct interpretation of any text because our knowledge and prejudices are constantly fueled by new information. There are several similarities between hermeneutical thinking and qualitative research, especially in the goal of gaining understanding through inquiry (Kinsella, 2006). Traditional Traditional qualitative research has its roots in empiricism and positivism dating back to the sixteenth century (Gale, 1979). Historically, between 1900 and 1950, qualitative researchers strived for objective, valid, timeless, positivist writings about their research that could often be overshadowed by personal beliefs, imperialism, and monumentalism (Denzin & Lincoln, 2000). Some researchers utilize poetry as a means of data representation in traditional qualitative studies. For example, poetic forms and structures can provide different emphasis when representing in-depth interviews; lyrical poems can be crafted from traditional interview texts; and poetry can be used as a means of coding and representing the interview (Furman, 2006a; Gallardo et al., 2009, p. 290; Poindexter 2002a; Richardson, 1993, 1994). Poetic inquiry To use poetic inquiry in research is to incorporate poetry in some way as an analytical device, whether in data collection, as a tool to view data in unique ways that can help yield new insight, or as a way of representing findings to peers and the general public. Using poetic inquiry as an analytical device can help bring focus to ideas and data that are most relevant to the research. The process of writing poetry or thinking poetically about the inquiry process helps us to collect the most relevant themes and phrases out of the sea of information available to us (Prendergast, 2009a). Poetic inquiry serves as a valuable tool to give us the ability to view our data, writing, and conclusions from more empathetic and creative perspectives. Poetic inquiry synthesizes experiences in a direct and affective way. This process of poetic synthesis can help uncover contradictions, missing information, and problems with internal validity. The utilization of poetry can augment data analysis in different ways. It can help the researcher think creatively about the data and results and lead them to uncover common themes among numerous pages of raw information. Poetry may also be used to communicate data, especially in situations where the researcher wishes to impart findings to a wider audience. Poetic representation can make raw facts more accessible to larger groups of people. Poetry can be used in data collection in many different ways. Tedlock (1983) transcribed participant data into “narrative poetry.” Gee (1985) devised a method of poetic transcription dividing interview data into quatrain stanzas. This early model pre-dates the most recent development of arts-based poetic inquiry. Poems can be gathered from specific groups to describe ethnographic data. They can be used as an interview tool, written by participants as answers to questions, or they can be the subject of study. Poetic inquiry tends to belong to one of the following categories, distinguished by the voice that is engaged: • Literature-voiced poems written from or in response to works of literature/theory; • Researcher-voiced poems framed in a research context which use field notes, journal entries, or reflective/ creative/ autobiographical/ autoethnographic writing as a data source; and • Participant-voiced poems written from interview transcripts or solicited directly from participants, sometimes in an action research model, where the poems are co-created with the researcher (Prendergast, 2009a, p. xxii). Exploration of poetic characteristics The main difference between traditional literary poetry and a research poem is in the position of the author to the data. The term [research poem] connotes the use of poetry less for expressive and literary means, and more for the purpose of generating or presenting data … [A research poem presents] data that remains faithful to the essence of the text, experience, or phenomena being represented. (Furman, Lietz, & Langer, 2006, p. 3) A variety of poetic forms, such as the Pantoum or Haiku or Tanka, which are rooted in Japanese poetic traditions, can be used in data analysis and presentation of qualitative data to the reader. Poetry can evoke emotion and promote thought about experiences that may be vastly different from our own (Furman et al., 2006).