Describe a Case Study from Clinician’s Guide to Evidence-Based Practices: Mental Health and the Addictions


Ethical, Legal, and Socio-cultural Considerations


SKU: Repo000652

Evidence-based Practice

Provide a brief overview of the main differences between quantitative and qualitative research approaches. Utilise scholarly support in this assignment to support your points versus using someone else’s words to make your points for you.  Also, remember HOW you write your paper will greatly affect WHAT your reader can take from your paper.  In other words, verify that you have correct grammar and that you have carefully proofed your paper for APA format errors and typos.

In this Application Assignment, you review some case studies related to evidence-based practice and search for published research that could guide your decision making. You also evaluate legal, ethical, and cultural implications for your decision.

Case Study

Select a case study from Clinician’s Guide to Evidence-Based Practices: Mental Health and the Addictions. Describe the case study and identify the critical elements that would require you to review published research to guide your practice. Using the Walden Library, cite and summarise a research study that informs evidence-based counselling practice related to the case study as it would occur in your specialisation area.

Ethical, Legal, and Socio-cultural Considerations

Next, explain any ethical, legal, and socio-cultural considerations that apply for the case and/or the research article selected. Remember this section is ethical, legal, and sociocultural so you need to discuss all three. In addition, you need to support your points with scholarly support, such as the ethical code, laws, etc.


Your conclusion section should recap the major points you have made in your work. However, perhaps more importantly, you should interpret what you have written and what the bigger picture is. Remember your paper should be 4 – 6 pages not counting your title page and reference page. Please do not exceed six pages of content.

The case of Francesco

He has no insurance coverage and little money. Even if he could obtain a few free or low –cost Sessions of out-patient therapy for his generalised anxiety disorder from his case manager, he would probably not receive a cutting edge, state –of-art(Evidence base practice( EBP) treatment. In all likelihood, Francesco would receive a prescription for an anti-anxiety medication from his personal care physician. His enrolling in an extensive alcohol rehabilitation program seems improbable given his lack of health insurance and inability to pay privately. Even if offered, Francesco’s low readiness to change would probably lead him to decline at this time. Moreover, Francesco’s chronic history of alcohol dependence, two previous inpatient rehabilitations, and current minimization of his substance abuse all conspire to lower his odds of a good prognosis without extensive treatment. Any EBP that does not consider the patient’s unique characteristics, culture and preferences is ripe for failure.

Learning Outcomes

By the end of this week, you should be able to:

Analyse the importance of critically analysing counselling research

Apply research that informs decision making in evidence-based practice to case studies

Analyse ethical, legal, and socio-cultural considerations in evidence-based practice

Addition article from the library database

The National Institutes of Health (NIH) has called for a focus on personalised medicine. Creating guidelines for the selection of treatments likely to yield the greatest efficacy based on an individual’s baseline characteristics should have a significant impact on improving the effectiveness of mental health treatment. In order to achieve this overarching goal, researchers must uncover pre-treatment variables (e.g., baseline demographics, clinical characteristics) that have a predictive relation with outcome measures. Two distinct approaches can be taken: (a) understanding which types of individuals will respond best to treatment, regardless of the nature of the treatment ( non-specific predictors); and (b) understanding which treatment works best for a particular individual ( moderators) (see Driessen, Cuijpers, Hollon, & Dekker, 2010; Fournier et al., 2009). General, non-specific predictors of outcome across treatment groups provide prognostic information by clarifying what types of patients will respond more or less favourably to treatment in general. Treatment moderators provide prescriptive information about optimal treatment selection. Moderators, as opposed to predictors, are more useful in identifying subgroups of patients who will respond differentially to one treatment over another, thereby increasing the utility of the findings in making treatment decisions ( Hollon & Najavits, 1988; Simon & Perlis, 2010). Thus, although there are benefits to identifying baseline predictors of overall treatment success (i.e., see Kraemer, Wilson, Fairburn, & Agras, 2002), identifying treatment moderators (who will do best in which treatment) may have more important clinical implications.

Researchers have attempted to address personalised medicine in the context of the treatment of depression ( Simon & Perlis, 2010), but much less has been done in the context of anxiety treatment. Although treatment efficacy for anxiety disorders is often good (e.g., Bergström et al., 2009; Westra, Arkowitz, & Dozois, 2009), a significant number of individuals drop out of treatment, need additional treatment, do not significantly improve, or show a return of symptoms at follow-up assessments (e.g., Hofmann, Schulz, Meuret, Moscovitch, & Suvak, 2006; van Apeldoorn et al., 2008). Given the high prevalence ( Kessler, Chiu, Demler, Merikangas, & Walters, 2005) and substantial cost ( Greenberg et al., 1999) of anxiety disorders, more work is needed to match patients to the appropriate treatments in order to improve overall efficacy.

Extant literature has focused primarily on general predictors of cognitive behavioural therapy (CBT) outcomes. CBT for anxiety disorders appears to work similarly across gender, age, and socioeconomic status (e.g., Piacentini, Bergman, Jacobs, McCracken, & Kretchman, 2002; Schuurmans et al., 2009; Watanabe et al., 2010). With respect to clinical variables, however, the outcomes are mixed. For example, the extent to which baseline severity of a disorder impacts outcome differs across studies ( Kampman, Keijsers, Hoogduin, & Hendriks, 2008; Meuret, Rosenfield, Seidel, Bhaskara, & Hofmann, 2010; Watanabe et al., 2010). Also, several studies have observed that comorbid depression does not predict anxiety symptom outcomes following CBT (e.g., Kampman et al., 2008; Rief, Trenkamp, Auer, & Fichter, 2000; Schuurmans et al., 2009; van Balkom et al., 2008), whereas others have found that it predicts worse outcomes ( Chambless, Beck, Gracely, & Grisham, 2000; Chambless, Tran, & Glass, 1997; Steketee, Chambless, & Tran, 2001; Watanabe et al., 2010).

For the most part, other (non-mood disorder) psychiatric comorbidity appears to have little to no influence on CBT outcomes for anxiety disorders ( Kampman et al., 2008; Mennin, Heimberg, & Jack, 2000; Ollendick, Ost, Rueterskiold, & Costa, 2010; Schadé et al., 2007; Turner, Beidel, & Dancu, 1996). There are some exceptions ( Steketee et al., 2001), such as the finding that certain additional anxiety disorders are associated with greater improvement in the targeted anxiety disorder (e.g., Brown, Antony, & Barlow, 1995). Finally, both poor health and high baseline neuroticism have been associated with a poorer prognosis from CBT for those with late-life anxiety ( Schuurmans et al., 2009).

To our knowledge, only one study has compared the effect of putative moderators on two distinct psychological treatments for anxiety disorders ( Meuret et al., 2010). In that study of treatment for panic disorder, lower perceived control at baseline was associated with poorer outcomes from a brief (4-week) treatment aimed at changing respiration as opposed to brief cognitive therapy, whereas higher levels of cognitive misappraisal of anxiety symptoms (i.e., anxiety sensitivity) at baseline were related to poorer outcomes in cognitive therapy as opposed to treatment aimed at changing respiration. No study has compared moderators between standard (i.e., 12-week) CBT and an established alternative treatment.

The goal of the current study was to evaluate potential moderators of a traditional full package of CBT compared to acceptance and commitment therapy (ACT) for anxiety disorders. Whereas CBT uses logical empiricism and exposure to feared stimuli in order to replace misappraisals with more evidence-based thinking, and aims to replace avoidance with approach behavior (see Craske, 2011), ACT uses cognitive defusion and acceptance to increase willingness to experience anxiety, and to engage in behavioral actions toward life values ( Hayes, Strosahl, & Wilson, 1999). There is growing interest in ACT as an alternative approach, and initial data indicate that ACT is an effective treatment for anxiety disorders (e.g., Arch, Eifert, Plumb, Rose, & Craske, 2012). Although CBT and ACT share some common elements ( Arch & Craske, 2008), they are derived from different theoretical models and involve different treatment strategies; thus, differential moderation may be expected.

Because CBT focuses on challenging cognitive misappraisals through cognitive and behavioral strategies, and because cognitive misappraisals of anxiety symptoms have been shown to mediate CBT outcomes ( Meuret, Rosenfield, Hofmann, Suvak, & Roth, 2009; Smits, Rosenfield, McDonald, & Telch, 2006), baseline level of cognitive misappraisals could be one moderator of treatment outcome, and may be more influential in CBT compared to ACT. However, the direction of that association is unclear. On the one hand, patients high in cognitive misappraisals may improve more with CBT than patients low in cognitive misappraisals, since the CBT focus upon cognitive restructuring would match the dysregulation underlying their anxiety; conversely, those low in baseline anxiety sensitivity may show poorer CBT outcomes because the treatment focus does not match the underlying dysregulation. On the other hand, those high in anxiety sensitivity at baseline may improve less in CBT because strongly held misappraisals may be resistant to change through direct attempts at challenging and replacing them with more evidence-based thinking. In support of the latter hypothesis, Meuret et al. (2010) found that higher levels of cognitive misappraisal of anxiety symptoms predicted poorer outcomes from cognitive therapy for panic disorder than lower levels. Additionally, both of these possibilities may be true: that a non-linear relation exists between baseline anxiety sensitivity and treatment outcome in CBT, with high and low levels of anxiety sensitivity associated with poorer outcome than moderate levels. There are no studies to our knowledge that have examined anxiety sensitivity, or any other clinical variable, as a non-linear predictor or moderator of treatment outcome. Exploring nonlinear associations between anxiety sensitivity and treatment outcome may improve our ability to uncover moderators and more precisely match patients with appropriate treatments. Based on the limited previous research, we speculated that higher baseline anxiety sensitivity would be associated with poorer outcome. However, we also considered the possibility that the association may be non-linear.

A potential moderator of ACT may be experiential avoidance. Because ACT focuses on increasing willingness to experience and accept negative emotion, and because willingness has been shown to mediate ACT outcomes ( Arch, Wolitzky-Taylor, Eifert, & Craske, 2012), those high in experiential avoidance may show more improvement in an ACT approach. In support, Zettle (2003) found that higher baseline levels of experiential avoidance positively predicted outcomes in a small sample of students with mathematics anxiety who were treated with ACT. Thus, it was hypothesised that higher experiential avoidance would be associated with more favourable outcomes in ACT. As with anxiety sensitivity, we explored the possibility that the association between baseline experiential avoidance and treatment outcome was non-linear in order to uncover potential moderating effects that may not be observed when looking only at linear associations. Given the lack of research exploring experiential avoidance as a predictor of outcome in CBT, no specific hypotheses were made.

Also, we hypothesised that ACT would outperform CBT among those with mood disorder comorbidity because ACT taps into constructs presumed to be shared across anxiety and mood disorders, whereas CBT is more disorder-specific in content (although its effects extend to comorbidity; e.g., Craske et al., 2007). Finally, given prior findings regarding predictors, we hypothesised that the presence of an additional anxiety disorder would not impact outcome across groups, whereas neuroticism and baseline severity of the principal disorder would be associated with poorer outcomes. No other specific hypotheses were made.

To test our hypotheses, we used a sample of patients with mixed principal anxiety disorders. Such an approach is in line with current directions toward a trans-diagnostic approach (e.g., Allen, McHugh, & Barlow, 2008; Barlow, Allen, & Choate, 2004), given the number of elements common across the anxiety disorders ( Craske et al., 2009). Moreover, identification of treatment moderators across a variety of anxiety disorders may be of greater clinical utility in high demand real-world practice settings than moderators for each anxiety disorder.


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