Supervisor variance in psychotherapy outcome in routine practice (psychothera...Daryl Chow
Objective: Although supervision has long been considered as a means for helping trainees develop competencies in their clinical work, little empirical research has been conducted examining the influence of supervision on client treatment outcomes. Specifically, one might ask whether differences in supervisors can predict/explain whether clients will make a positive or negative change through psychotherapy. Method: In this naturalistic study, we used a large (6521 clients seen by 175 trainee therapists who were supervised by 23 supervisors) 5-year archival data-set of psychotherapy outcomes from a private nonprofit mental health center to test whether client treatment outcomes (as measured by the OQ-45.2) differed depending on who was providing the supervision. Hierarchical linear modeling was used with clients (Level 1) nested within therapists (Level 2) who were nested within supervisors (Level 3). Results: In the main analysis, supervisors explained less than 1% of the variance in client psychotherapy outcomes. Conclusions: Possible reasons for the lack of variability between supervisors are discussed.
The Carlat Psychiatry Report (Interview with Scott Miller, April 2015)Scott Miller
The April 2015 issue of the Carlat Psychiatry Report, an unbiased report/review of "all things psychiatric." The issue contains a lengthy interview with Dr. Scott Miller on the subject of top performing clinicians.
Supervisor variance in psychotherapy outcome in routine practice (psychothera...Daryl Chow
Objective: Although supervision has long been considered as a means for helping trainees develop competencies in their clinical work, little empirical research has been conducted examining the influence of supervision on client treatment outcomes. Specifically, one might ask whether differences in supervisors can predict/explain whether clients will make a positive or negative change through psychotherapy. Method: In this naturalistic study, we used a large (6521 clients seen by 175 trainee therapists who were supervised by 23 supervisors) 5-year archival data-set of psychotherapy outcomes from a private nonprofit mental health center to test whether client treatment outcomes (as measured by the OQ-45.2) differed depending on who was providing the supervision. Hierarchical linear modeling was used with clients (Level 1) nested within therapists (Level 2) who were nested within supervisors (Level 3). Results: In the main analysis, supervisors explained less than 1% of the variance in client psychotherapy outcomes. Conclusions: Possible reasons for the lack of variability between supervisors are discussed.
The Carlat Psychiatry Report (Interview with Scott Miller, April 2015)Scott Miller
The April 2015 issue of the Carlat Psychiatry Report, an unbiased report/review of "all things psychiatric." The issue contains a lengthy interview with Dr. Scott Miller on the subject of top performing clinicians.
Article in Division 29's journal, psychotherapy that reviews the research on routine outcome monitoring, arguing that current efforts are at risk for repeating the history of failed efforts to improve the outcome of psychotherapy.
What are the Core Tasks of Psychotherapy? A Presentation for the 2013 Evoluti...Scott Miller
A summary of a presentation delivered by Scott D. Miller, Ph.D. at the 2013 Evolution of Psychotherapy conference in Anaheim, California. It contrasts traditional ideas with empirically supported practices.
PCOMS: A Viable Quality Improvement Strategy for Public Behavioral HealthBarry Duncan
This is the latest from the research team of the Heart and Soul of Change Project, published in the Journal of Consulting and Clinical Psychology. This study demonstrated that PCOMS is not only a viable quality improvement strategy but also that services to the poor and disenfranchised provided in a public behavioral setting, contrary to earlier research, can be as effective as those delivered in randomized clinical trials.
How to Improve Quality of Services by Integrating Common Factors into Treatme...Scott Miller
Presentation by Dr. Bruce Wampold about how the outcome and quality of psychotherapy can be improved by adding common factors to the treatment. Wampold documents the lack of difference in outcome between competing treatment methods AND the relatively large contribution made by common factors to outcome.
Although many of you may not be interested in the psychometric details of the ORS and SRS, it does bear importantly on whether there are seen as credible. Jeff Reese and I (Duncan & Reese, 2013) recently exchanged views with Halstead, Youn, and Armijo (2013), debating when a measure is too brief and when it is too long. Here is our paper. First regarding when a measure is too brief: There is no doubt that 45 items, 30 items, or even 19 items is psychometrically better than 4 items, and that the increased reliability and validity of longer measures likely result in better detection, prediction, and ultimate measurement of outcome. But how much better is the really the question. Are these differences clinically meaningful and do they offset the low compliance rates and resulting data integrity issues from missing data? These are the questions that require empirical investigation to determine how brief is too brief, although from my experience, the verdict has already been rendered. But when is a measure too long? The answer is simple: When clinicians won’t use it.
Summary of SAMHSA's review of and listing of feedback Informed Treatment as an evidence-based practice. The International Center for Clinical Excellence received perfect scores for readiness for dissemination materials
Utilizing the ORS in a Community Mental Health Clinic (student outcomes etc) ...Scott Miller
Outcomes of students and professionals were compared. Study found students equally effective or better than experienced therapists when students used the measures. Students who used the measures faithfully to guide practice had significantly better outcomes
Generalist Practice A Presentation on Steps of The Problem-SolvMatthewTennant613
Generalist Practice: A Presentation on Steps of The Problem-Solving Process
Name:
Date: May 5th, 2021
Pamela Easter !!!
1
Problem Identification or Engagement
Identify the type of problem – drug addiction and its effects on Family members.(Janice Walker is at the center of problem)
Identify how the problem has affected the rest of the community- substance abuse effects cost in the community in measurable ways including loss of productivity and unemployability; impairment in physical and mental health; reduced quality of life; increase violence; and Crime; abuse and neglect of children.
The community's general perception of the problem- the economic consequences of drug abuse severely burden federal , State, and local government resources, and the taxpayer.
The root cause of the problem- most often the cause of addiction is chronic stress, a history of trauma (PTSD), mental illness, lastly family history with addiction
Problem Identification/Engagement. The first step is to identify the type of problem the researcher is dealing with. Identify how the problem has affected the rest of the community and the community's general perception of the problem. The next step is to evaluate how the problem has affected the community or a client. Lastly, identify the root cause of the problem; where did the problem come from, or how did the victim get the problem they are facing.
2
Data Collection
Three major ways of collecting data from Clients are Interviews, Observation, and surveys
Interviews:
Engaging with the client one-on-one. Listening to client’s perspective of the problem
This method has ability to untangle the individual's problem, emotions, background, and the general social context, The Advocate can get the client's perception of the treatment
Observation:
It obtains data from clients by assessing the reaction to their respective environments
The researcher can identify factors contributing to the Client's condition
Data collection. There are many ways to collect data from a client. Three major ways of collecting data from a client are Interviews, Observation, and First Extraction.
Interviews: The researcher can speak with the patient on a one-on-one basis. A researcher or a nurse can use this method because of its ability to untangle the individual's problem, emotions, background, and the general social context, which in this paper largely revolves around the family relationship (Cohen et al., 2017). This data collection method paves the way for a nurse to get the patient's perception of the treatment and some of the elements that motivated them to embrace treatment.
Observation: it is used to obtain data from clients by assessing the reaction to their respective environments. Observation methods allow the researcher to identify social dimensions and family background factors contributing to the patient's condition.
3
Data Collection cont’d
Surveys : Can discover the problem
Clients Family History background details o ...
Article in Division 29's journal, psychotherapy that reviews the research on routine outcome monitoring, arguing that current efforts are at risk for repeating the history of failed efforts to improve the outcome of psychotherapy.
What are the Core Tasks of Psychotherapy? A Presentation for the 2013 Evoluti...Scott Miller
A summary of a presentation delivered by Scott D. Miller, Ph.D. at the 2013 Evolution of Psychotherapy conference in Anaheim, California. It contrasts traditional ideas with empirically supported practices.
PCOMS: A Viable Quality Improvement Strategy for Public Behavioral HealthBarry Duncan
This is the latest from the research team of the Heart and Soul of Change Project, published in the Journal of Consulting and Clinical Psychology. This study demonstrated that PCOMS is not only a viable quality improvement strategy but also that services to the poor and disenfranchised provided in a public behavioral setting, contrary to earlier research, can be as effective as those delivered in randomized clinical trials.
How to Improve Quality of Services by Integrating Common Factors into Treatme...Scott Miller
Presentation by Dr. Bruce Wampold about how the outcome and quality of psychotherapy can be improved by adding common factors to the treatment. Wampold documents the lack of difference in outcome between competing treatment methods AND the relatively large contribution made by common factors to outcome.
Although many of you may not be interested in the psychometric details of the ORS and SRS, it does bear importantly on whether there are seen as credible. Jeff Reese and I (Duncan & Reese, 2013) recently exchanged views with Halstead, Youn, and Armijo (2013), debating when a measure is too brief and when it is too long. Here is our paper. First regarding when a measure is too brief: There is no doubt that 45 items, 30 items, or even 19 items is psychometrically better than 4 items, and that the increased reliability and validity of longer measures likely result in better detection, prediction, and ultimate measurement of outcome. But how much better is the really the question. Are these differences clinically meaningful and do they offset the low compliance rates and resulting data integrity issues from missing data? These are the questions that require empirical investigation to determine how brief is too brief, although from my experience, the verdict has already been rendered. But when is a measure too long? The answer is simple: When clinicians won’t use it.
Summary of SAMHSA's review of and listing of feedback Informed Treatment as an evidence-based practice. The International Center for Clinical Excellence received perfect scores for readiness for dissemination materials
Utilizing the ORS in a Community Mental Health Clinic (student outcomes etc) ...Scott Miller
Outcomes of students and professionals were compared. Study found students equally effective or better than experienced therapists when students used the measures. Students who used the measures faithfully to guide practice had significantly better outcomes
Generalist Practice A Presentation on Steps of The Problem-SolvMatthewTennant613
Generalist Practice: A Presentation on Steps of The Problem-Solving Process
Name:
Date: May 5th, 2021
Pamela Easter !!!
1
Problem Identification or Engagement
Identify the type of problem – drug addiction and its effects on Family members.(Janice Walker is at the center of problem)
Identify how the problem has affected the rest of the community- substance abuse effects cost in the community in measurable ways including loss of productivity and unemployability; impairment in physical and mental health; reduced quality of life; increase violence; and Crime; abuse and neglect of children.
The community's general perception of the problem- the economic consequences of drug abuse severely burden federal , State, and local government resources, and the taxpayer.
The root cause of the problem- most often the cause of addiction is chronic stress, a history of trauma (PTSD), mental illness, lastly family history with addiction
Problem Identification/Engagement. The first step is to identify the type of problem the researcher is dealing with. Identify how the problem has affected the rest of the community and the community's general perception of the problem. The next step is to evaluate how the problem has affected the community or a client. Lastly, identify the root cause of the problem; where did the problem come from, or how did the victim get the problem they are facing.
2
Data Collection
Three major ways of collecting data from Clients are Interviews, Observation, and surveys
Interviews:
Engaging with the client one-on-one. Listening to client’s perspective of the problem
This method has ability to untangle the individual's problem, emotions, background, and the general social context, The Advocate can get the client's perception of the treatment
Observation:
It obtains data from clients by assessing the reaction to their respective environments
The researcher can identify factors contributing to the Client's condition
Data collection. There are many ways to collect data from a client. Three major ways of collecting data from a client are Interviews, Observation, and First Extraction.
Interviews: The researcher can speak with the patient on a one-on-one basis. A researcher or a nurse can use this method because of its ability to untangle the individual's problem, emotions, background, and the general social context, which in this paper largely revolves around the family relationship (Cohen et al., 2017). This data collection method paves the way for a nurse to get the patient's perception of the treatment and some of the elements that motivated them to embrace treatment.
Observation: it is used to obtain data from clients by assessing the reaction to their respective environments. Observation methods allow the researcher to identify social dimensions and family background factors contributing to the patient's condition.
3
Data Collection cont’d
Surveys : Can discover the problem
Clients Family History background details o ...
The first quasiexperiemental study of the ORS/SRS in a telephonic EAP company. Doubled outcomes and improved retension. Set the stage for the RCTs that followed
Research Methods in PsychologyThe Effectiveness of Psychodyn.docxronak56
Research Methods in Psychology
The Effectiveness of Psychodynamic Therapy on Childhood Abused Victims.
Annotate Bibliography
Nickel, R., & Egle, U. T. (2005). Influence of childhood adversities and defense styles on the 1-year follow-up of psychosomatic- psychotherapeutic inpatient treatment. Psychotherapy Research, 15(4), 483-494. doi:10.1080/10503300500091660
This study was conducted to examine childhood abused victims' quality of life before treatment, and after treatment. A multimodal psychodynamic group concept was used to treat 138 patients for an average of 80.4 days. In order, to properly develop a comparison, clinical examiners interviewed each patient before the start to their in-patient program to diagnose each individual, and have therapy goals for them. The questionnaires that were used to measure quality of life targeted the patient's social life health, general health, emotional and physical functioning. The patients were all re-assessed after one year had passed by, to examine any changes in their quality of life. It was revealed that the patients' mental quality of life had improved by more than one standard deviation. Their physical quality of life improved just less than one standard deviation.
Baker, V., & Sheldon, H. (2007). 'The Light at the End of the Tunnel': Issues of Hope and Loss in Endings with Survivors Groups. Group Analysis, 40(3), 404-416. doi:10.1177/0533316407081759
In this article, a previous study was mentioned in which childhood abuse survivors were treated for 20 sessions. The authors of this article agreed that 20 sessions of treatment for childhood abuse survivors isn't enough for them to develop a secure attachment to the group. This study explores whether childhood abuse victims can benefit from treatment by being treated for a longer period. Seven group members of ages 23-55 were treated for 52 sessions, over a period of 13 months. All of the members in this group are women, and they had all been sexually abused in their childhood by a family member. They followed a psychodynamic, time-limited closed group therapy, in which they all benefited somewhat. However, many of the members expressed anxiety of leting go, and not being able to move on after the group ended.
Foa, E. (2009). Psychodynamic Therapy for Child Trauma. Retrieved from https://www.istss.org/ISTSS_Main/media/Documents/ISTSS_g12.pdf
The authors of this article discuss the efficacy of pyshcodynamic methods on childhood abuse victims, by presenting the results of five randomized controled trials. The population involved in these randomized controled trials were : preschoolers that were exposed to domestic violance, abused infant and sexually abused girls. Three of these randomized controled trials focused on a child-parent therapy, using a relationship-based intervention. The goal to these three randomzied trials was to strengthen the parent-child relationship to lead to a long term healthy child development. Another randomized ...
Week 5 EBP ProjectAppraisal of EvidenceCLC EBP Research .docxcockekeshia
Week 5 EBP Project/Appraisal of Evidence
CLC: EBP Research Table
Citation
Include the APA reference note.
Abstract/Purpose
Craft a 100-150 word summary of the research.
Research/Study
Describe the design of the relevant research or study in the article.
Methods
Describe the methods used, including tools, systems, etc.
Setting/Subject
Identify the population and
the setting in which the study was conducted.
Findings/Results
Identify the relevant findings, including any specific data points that may be of interest to your EBP project.
Variables
Describe the independent and dependent variables in the research/study.
Implication for Practice
Articulate the value of the research to the EBP project your group has chosen.
Independent Variable
Dependent Variable
King-Shier, K.M., Mather, C., &LeBlanc, P. (2013). Understanding the influence of urban or rural living on cardiac patients’ decisions about diet and physical activity: Descriptive decision modeling. International Journal of Nursing Studies, 50(11), 1513-1523. doi: 10.1016/j.ijnurstu.2013.03.003
This research aims to answer to better understand the decision-making process of eating a heart healthy diet and extent of physical activity. Also, are these decisions influenced by whether the subject lives in a rural or urban setting. The research proposal was the cultural issues effected participants decision making as well as place of residence. This research used a previous qualitative research design in which 42 cardiac patients (21 urban, and 21 rural) were interviewed about their diet and physical activity. The researchers then designed a model for interviewing regarding the decision-making process. The combination model was then given and tested with 647 cardiac patients (327 urban and 320 rural) from Canada. The results were based on 93.5% accuracy for diet and 97.5 % accuracy with physical activity. Results indicated that decision-making was less about place of residence and more about perception of control over health including time, effort, or competing priorities, receipt of appropriate clear information, and appeal of the activity.
A three-staged, multi-methods approach was used to develop and analyze the descriptive decision making model that patients use in making decisions regarding their cardiac lifestyle. A cross-sectional survey was used to interview patients one year post-cardiac catherization. These interviews were performed via telephone. A three stage decision tree model was then used to analyze the information offered. The stages were as follows: 1. Factors that were influential in decision making. 2. If and where failure had occurred for patients. 3. Did patients consistently, sometimes, or not at all engage in physical activity and a heart healthy diet. Results were then analyzed using statistical analysis.
Information was gathered from a previous series of qualitative interviews conducted with 42 cardiac patients (21 rural, 21 urban). Based on the infor.
Slone, N. C., Reese, R. J., Mathews-Duvall, S., & Kodet, J. (2015). Evaluating the Efficacy of client feedback in group psychotherapy. Group Dynamics: Theory, Research, and Practice, 19, 122-136. doi:10.1037/gdn0000026
Running head SEARCHING AND CRITIQUING THE EVIDENCE1SEARCHING .docxtoltonkendal
Running head: SEARCHING AND CRITIQUING THE EVIDENCE 1
SEARCHING AND CRITIQUING THE EVIDENCE 4
Searching and Critiquing the Evidence
Student’s Name
Institution
Date
Searching and Critiquing the Evidence
There are various research studies that have been done on the outcome of self-care on Type 2 Diabetes Mellitus patients. In most of the studies, the most prevalent results are that self-care is an effective method of improving the health and lifestyle outcomes of Type 2 Diabetes patients. Krishna and Boren (2008) conducted a systematic review of evidence-based studies done between 1996 and 2007. The study analyzed 18 researches done within the selected time period and found that using phone calls and text messages to assist diabetes patients could improve the self-management outcomes. Shrivastava et al. (2013) analyzed the effectiveness of self-management for the diabetes mellitus patients. The study found that self-care helps to reduce the rate of morbidity and mortality among diabetes patients.
In addition, Steinsbekk et al. (2013) conducted a meta-analysis comparing the differences between the outcomes of group based self-management education and routine treatment for Type 2 diabetes patients. The study analyzed 21 studies that included studied on 2833 participants. The results of the meta-analysis showed that group-based self-management education helped to improve the psychosocial, clinical, and lifestyle outcomes among the diabetes patients. Lastly, Tang et al. (2008) examined the impact of social support and quality of life on the self-care behaviors of African American Type 2 diabetes patients. The study followed an observational design with 89 African-American adults, who were aged 40 and above. The study found that social support is vital for self-management to be effective in diabetes treatment.
The selected studies have helped to strengthen the merit of my selected theoretical framework. The theory selected for the study was Dorothea Orem’s Self Care Theory. These studies have helped to demonstrate some important evidence-based facts about the effectiveness of self-care for diabetes patients hence helping to prove the credibility of the theory. The scrutiny of these studies has helped to discover the degree of effectiveness of this theory and the best application methods that can make it an effective approach to improving the outcomes of patients with Type 2 Diabetes Mellitus.
Levels of Evidence in the Articles
The classification of the level of evidence of a given research is important in evidence-based studies because they help to show how accurate, credible, or reliable a research is (Gray, Grove & Sutherland, 2017). The most prevalent evidence in the research articles analyzed is Level II evidence. Level II evidence is one that is obtained from at least one randomized control trial (Moran, Burson & Conrad, 2017). The articles by Krishna and Boren (2008) and Steinsbekk et al. (2013) conducted meta-analyses of various rese ...
Literature ReviewA search was conducted using electronic database.docxssuser47f0be
Literature Review:
A search was conducted using electronic databases in the fields of nursing, medicine, education, psychology, and sociology. Using ProQuest Direct and EBSCO search engines, the following databases were accessed: CINAHL (Cumulative Index to Nursing and Allied Health Literature), MEDLINE in PubMed, Ovid, and PsycINFO. The search terms were grouped in the following key concepts: (a) occupational stress in nursing, (b) stress perception in nursing, (c) occupational stressors in nursing, (d) nursing generational diversity, and (e) coping in nursing. In a commentary on patient safety in nursing practice from the Agency for Healthcare Research and Quality, Hughes and Clancy7 reported that complexity and bullying represent 2 clear examples of nurse stressors. Li and Lambert8 concluded that nurses who are more satisfied with their job are more likely to remain in the workforce and to be committed to delivering high-quality patient care. Hall9 found that healthcare professions have some unique characteristics leading to occupational stress including physical responsibility for people, potential catastrophic effects on the patient and the employee, frequent exposure to pain and suffering, and exposure to infectious diseases and potential hazardous substances. Hamaideh et al10 identified that death and dying were the strongest stressors perceived by Jordanian nurses. In this study, workload and guidance were found to be the most supportive behaviors provided to nurses facing stress followed by emotional support.10
Carver and Candela11 concluded that considering the global nursing shortage, managers should increase their knowledge of the generational diversity. It is suggested that understanding how to relate to multiple generations can lead to improved nursing work environments.11 Repar and Patton12 found that the combined effects of compassion fatigue, chronic grief, and emotional and physical exhaustion led to significant burnout and prolonged job dissatisfaction in the nursing profession. In this study, using guided sessions, a massage therapist gave 10-minute chair massages, and a visual, language, or musical artist engaged participants in imaginative and creative activities such as poetry reading, free writing, guided imagery, and listening to live music.12 The results suggest that the activities reduce some of the unpleasant, stressful, and tension-producing emotions that nurses typically experience at work, leaving them more peaceful and energized.12 Based on the findings of this review of the literature, it is recognized that stress is a major component of nursing and can be detrimental to nurse retention. In addition, most studies identified some differences that exist between the present generational nursing cohorts in terms of values and beliefs. No studies were identified reporting how work-related stress affects different generations of nurses, how the generations perceive stress, and what coping styles are used.
Study Des ...
Background: Behavioral health conditions are prevalent among patients in inpatient medical settings and when not adequately treated contribute to diminished treatment outcomes and quality of life. Substantial evidence has demonstrated the effectiveness of psychological interventions in addressing behavioral health conditions in a range of settings but, to a lesser extent with psychologically-based interventions delivered in inpatient medical settings. Purpose: The purpose of this paper is to increase attention on psychological interventions being delivered to patients across a broad spectrum of medical specialties in inpatient medical settings to support the implementation of interventions to address increasing patient needs. Methods: This selected, brief review of the literature sought to describe published psychologically-based interventions delivered in inpatient medical settings. A search for studies catalogued on PubMed from 2007 to 2016 was examined and studies were included in the review if they were delivered within inpatient medical settings. Two reviewers independently assessed relevant studies for criteria. Results: A total of ten articles met the inclusion criteria with interventions targeting outcomes across four primary domains: 1) pain and fatigue; 2) cognition; 3) affective/emotional and; 4) self-harm. Several articles support interventions grounded in Cognitive-Behavioral Therapy and brief psychological interventions. Most studies reported favorable outcomes for the interventions relative to controls. Conclusions: Psychologically-based interventions, especially those that integrate components of cognitive-behavioral therapy and a multidisciplinary approach, can be implemented in inpatient medical settings and may promote improved patient outcomes. However, the quality of this evidence requires formal assessment, requiring more comprehensive reviews are needed to replicate findings and clarify effectiveness of interventions.
Running head VETERANS PTSD CAUSES, TREATMENTS, AND SUPPORT SYSTEM.docxjenkinsmandie
Running head: VETERANS PTSD CAUSES, TREATMENTS, AND SUPPORT SYSTEMS 1
VETERANS PTSD CAUSES, TREATMENTS, AND SUPPORT SYSTEMS 3
Veterans PTSD Causes, Treatments, and Support systems
Veterans PTSD Causes, Treatments, and Support systems
Evaluations on Post Traumatic Stress Disorder (PTSD) among veterans is imperative for a positive health outcome. The evaluations and analysis of the results ensure that barriers to treatment are addressed and have access to the available support systems. Studies carried out have depicted the successes of the treatments and support programs in the health systems to veterans. Modifications on the systems have also been recommended to combat and control PTSD. Alternative approaches such as computerized systems, natural treatment methods, and home-based systems are also essential in providing a holistic approach in PTSD treatments. Treatment methods success ensures that veterans do not fall victim to depression, which can result in chronic diseases. This can be as a result of negative health behaviors and lifestyles. Understanding the consequences of PTSD among veterans will ensure that approaches utilized offer not only treatment methods but also offer support systems for general wellbeing.
The first source focuses on the treatment and success of three-week outpatient program by “evaluating patterns and predictors of symptom change during a three-week intensive outpatient treatment for veterans with PTSD.” The study is evidence-based on statistics drawn from the program and modifications for optimal success rates. 191 veterans were the participants in the research comprising of a daily group and individual Cognitive Processing Therapy (Zalta et al., 2018). The data was analyzed from the sample cohorts in accordance with military and demographic characteristics. Measures in the study involved treatment engagement as well as comparison of pre-treatment and post-treatment changes (Zalta et al., 2018). The results showed progress in the evaluation of predictors and patterns in treatment changes. Procedures utilized involved group sessions with daily activities for the development of the treatment program. Self-report metrics were also applied in the procedures as control groups were challenging in the study. Modified and intensive outpatient (IOP) treatment to veterans showed high success levels in the program (Zalta et al., 2018).
The second source examines a new treatment in exploring the feasibility of computerized, placebo-controlled, and home-based executive function training (EFT) on psychological and neuropsychological functions. The source titled “Computer-based executive function training for combat veterans with PTSD” shows trials in assessing feasibility and predictors output. The study shows how the functions can be useful in brain activation combating PTSD in veterans. Symptoms experienced after treatment on PTSD cases are stimulated through neural and cognition reactivity, which can be contr.
1PAGE 21. What is the question the authors are asking .docxfelicidaddinwoodie
1
PAGE
2
1. What is the question the authors are asking?
They asked about a reduction in judgmental biases regarding the cost and probability associated with adverse social events as they are presumed as being mechanisms for the treatment of Social Anxiety Disorder (SAD). Also, the authors poised on the changes in judgmental biases as mechanisms to explain cognitive-behavioral therapy for social anxiety disorder. On top of that, they stated that methodological limitations extant studies highlight the possibility that rather than causing symptom relief, a significant reduction in judgmental biases tends to be consequences of it or correlate. Considerably, they expected cost bias at mid-treatment to be a predictor of the treatment outcome.
2. Why do the authors believe this question is important?
According to the authors, this question was relevant as methodological limitations of present studies reflect on the possibility that instead of causing symptom belief, a significant reduction in judgmental biases can be consequences or correlated to it. Additionally, they ought to ascertain the judgment bias between treated and non-treated participants. Significantly, this was important as they had to determine the impact of pre and post changes in cost and probability of the treatment outcomes. But, probability bias at mid-treatment was a predictor of the treatment outcome contrary to the cost bias at mid-treatment that could not be identified as a significant predictor of the treatment outcome.
3. How do they try to answer this question?
They conducted a study to evaluate the significant changes in judgmental bias as aspects of cognitive-behavioral therapy for social anxiety disorders. To do this, they conducted a study using information from two treatment studies; an uncontrolled trial observing amygdala activity as a response to VRE (Virtual Reality Exposure Therapy) with the use of functional magnetic resonance imaging and a randomized control trial that compared Virtual Reality Exposure Therapy with Exposure Group Therapy for SAD. A total of 86 individuals who met the DSM-IV-TR criteria for the diagnosis of non-generalized (n=46) and generalized (n=40) SAD participated. After completing eight weeks of the treatment protocol, the participants who identified public speaking as their most fearsome social situation were included. The SCID (Structured clinical interview for the DSM-IV) was used to ascertain diagnostic and eligibility status on Axis 1 conditions within substance abuse, mood and anxiety disorder modules. The social anxiety measures were measured with the use of BFNE (Brief Fear of Negative Evaluation), a self-reporting questioner that examined the degree to which persons fear to be assessed by other across different social settings. Additionally, the OPQ (Outcome Probability Questionnaire) self-reporting questionnaire was used to evaluate individual’s estimate on the probability that adverse, threatening events will occur at t ...
REVIEW Open AccessWhat happens after treatment Asystema.docxmichael591
REVIEW Open Access
What happens after treatment? A
systematic review of relapse, remission, and
recovery in anorexia nervosa
Sahib S. Khalsa1,2*, Larissa C. Portnoff3, Danyale McCurdy-McKinnon4 and Jamie D. Feusner5
Abstract
Background: Relapse after treatment for anorexia nervosa (AN) is a significant clinical problem. Given the level of
chronicity, morbidity, and mortality experienced by this population, it is imperative to understand the driving forces
behind apparently high relapse rates. However, there is a lack of consensus in the field on an operational definition
of relapse, which hinders precise and reliable estimates of the severity of this issue. The primary goal of this paper
was to review prior studies of AN addressing definitions of relapse, as well as relapse rates.
Methods: Data sources included PubMed and PsychINFO through March 19th, 2016. A systematic review was
performed following the PRISMA guidelines. A total of (N = 27) peer-reviewed English language studies addressing
relapse, remission, and recovery in AN were included.
Results: Definitions of relapse in AN as well as definitions of remission or recovery, on which relapse is predicated,
varied substantially in the literature. Reported relapse rates ranged between 9 and 52%, and tended to increase
with increasing duration of follow-up. There was consensus that risk for relapse in persons with AN is especially
high within the first year following treatment.
Discussion: Standardized definitions of relapse, as well as remission and recovery, are needed in AN to accelerate
clinical and research progress. This should improve the ability of future longitudinal studies to identify clinical,
demographic, and biological characteristics in AN that predict relapse versus resilience, and to comparatively
evaluate relapse prevention strategies. We propose standardized criteria for relapse, remission, and recovery, for
further consideration.
Keywords: Anorexia nervosa, Treatment, Outcome, Relapse, Remission, Recovery, Prevention, Eating disorder,
Bulimia nervosa
Plain English Summary
Relapse occurs frequently in individuals receiving treat-
ment for anorexia nervosa. However, there is no com-
mon agreement on how to define relapse. In this study,
we reviewed previous studies of relapse, remission, and
recovery following treatment for anorexia nervosa. We
found that there were many different definitions for
these terms, which resulted in different estimates of re-
lapse rate. To understand what drives relapse it is
important to have a consistent definition across studies.
To help this discussion we propose common criteria for
relapse, remission, and recovery from anorexia nervosa.
Background
Anorexia nervosa (AN) is a serious psychiatric illness
with amongst the highest mortality rates of any mental
disorder—up to 18% in long-term follow-up studies [1–
3]. Most cases emerge during adolescence, and tend to-
wards a protracted and chronic course [4, 5]. In females,
AN has a p.
"Validity, Reliability and Factor Structure of the Mindfulness based Self-Efficacy Scale (MSES)", presented at the National conference of the New Zealand Psychological Society, 21 April 2012: Existing self-report questionnaires have been criticised for several reasons. Presents on a new self-report questionnaire to measure self-efficacy before, during and after mindfulness-based therapy or mindfulness training outside the therapy context. To try the MSES online and obtain instant results (at no cost), follow the link: http://www.mindfulness.net.au/mses
1. Modeling Mental Health Recovery:
From Psychometric Properties to Analysis of a Hierarchical Linear Growth Model
Karen Traxler, M.S., Suzy Landram, M.S., Tyler Kincaid, M.S., & Lisa Rue, Ph.D.
Applied Statistics and Research Methods
University of Northern Colorado
ASA: Women In Statistics; Raleigh Durham, North Carolina: May 15-17, 2014
Abstract Results & DiscussionMethods
Purpose
Participants
All participants were residents of CooperRiis Healing
Farm in North Carolina
Demographics
Data Analysis: 5 waves of data
• A two-level Hierarchical Linear Growth Curve
Model (HLM) was used to assess mental health
recovery over time. Five time periods (or waves of
data) were used: admission, 3 months, 6 months,
9 months, and 12 months.
• The strength of a HLM Growth Curve Model over
other regression models is that it can differentiate
between individual starting points (intercepts) as
well as individual change/recovery over time
(slopes) (Raudenbush & Bryk, 2002).
• Both HLM 7 and SPSS 20 (PASW, 2012) were used
for data analysis and the results were comparable
Waves of Data
Procedures
• Grand mean centering
was applied to an
individual’s MHRM-R
scores at each wave
point to simplify the
interpretation of scores
• Maximum Likelihood Estimation (MLE) was used
for all analysisBased on the RCC approach to mental health recovery, the purpose of this
study was to:
(a) Examine the psychometric properties of the Mental Health Recovery
Measure-Revised (MHRM-R) collected from a target population of
individuals with severe and persistent mental health conditions
(b) Assess whether mental health recovery outcomes, based on the MHRM-
R improved over time for the target population
(c) Investigate the roles age, gender, and/or primary diagnosis play in
positive mental health recovery outcomes over time
Target Population: Individuals with Severe and Persistent
Mental Health Conditions
Statistically modeling mental health recovery for individuals with severe
and persistent mental conditions has traditionally been accomplished
using the medical model of recovery which encompasses the elimination
or reduction of symptoms through medication and/or hospitalization. A
more holistic approach gaining support among both clinicians and
consumers is the Recovery-Centered Collaborative Approach to
mental health recovery (the RCC model) which is a person-centered
approach integrating medication with spirituality, hope, physical
wellbeing, life skills, strategies for managing symptoms, and strong
community and family support. Methods: Data from CooperRiis Healing
Farm, a residential treatment facility for individuals with severe and
persistent mental health conditions, specializing in the RCC approach
were examined using a Hierarchical Linear Growth Model (HLGM) to
assess recovery over a twelve month time period. Data included
variables such as age upon admission, gender, and primary diagnosis.
Results: The results from level 1 of the HLGM provided evidence that
there was significant positive growth in recovery scores over time.
Results from level 2 of the HLGM revealed that (1) individuals with
schizophrenia or other psychotic disorders had a significantly higher
recovery score upon admission than individuals with other diagnoses,
suggesting lack of insight into their disorder, (2) females recovered
significantly faster than males, regardless of age or primary diagnosis, (3)
individuals with a diagnosis of personality disorder or substance abuse
recovered ____ than individuals diagnosed with bipolar disorder,
depression, anxiety, attention deficit disorder, or schizophrenia and other
psychotic disorders.
Special thanks to:
Dr. Sharon Young &
Matt Snyder, M.S., M.A., L.P.C.
CooperRiis Healing Community
Special thanks to:
Dr. Susan Hutchinson
University of Northern Colorado
Research Questions
• Interactions: Interactions of independent variables were tested and no
significance was found
• Proportion of Variance Explained: The primary diagnosis of the residents (i.e.,
schizophrenic or not) explains 15.3% of the parameter variance in the initial status
(i.e., where a given resident baseline recovery score will start) and gender
accounts for 10.5% of the parameter variance in growth rates of recovery over
time.
• Final Estimates HLM Growth Model Gender & Primary Diagnosis
Recovery Over Time
Implications for Mental Health Researchers, Clinicians, & Consumers
Implications for Mental Health Researchers, Clinicians, and Consumers
• The MHRM-R is an good measure of mental health recovery and would be appropriate for
researchers and clinicians to utilize in the target population
• This study supports the holistic recovery-centered collaborative approach to recovery as a
viable alternative to the medical model of mental health recovery, even in patients with the
most severe and persistent mental health disorders
• Applied researchers and clinicians can use this information to develop appropriate person-
centered treatments for severe mental health conditions
• Individuals seeking mental health treatment for acute symptoms can have a voice in their
recovery process and maintain hope throughout their journey of symptom management.
Limitations
Recovery is a complex process involving far more than age, gender, and a dichotomized primary
diagnosis, therefore significant limitations are inherent in any explanatory model of recovery over time
• Research Question # 1: Is the MHRM-R a reliable and valid measure of
mental health recovery in the target population?
• Research Question # 2: Does mental health recovery of individuals with
severe and persistent mental health conditions, receiving a recovery-
centered collaborative mental health intervention, improve over time?
o Is there a difference in recovery outcomes over time based on:
• Research Question # 3: gender?
• Research Question # 4: primary diagnosis?
• Research Question # 5: age?
Instrumentation
Mental health recovery was measured using the
Mental Health Recovery Measure-Revised
(MHRM-R: Young & Ensing, 2003). The MHRM-R
is a 30-item self-report survey with a traditional
five-point Likert response scale ranging from 1=
strongly disagree to 5 = strongly agree. Higher
scores indicate more positive recovery outcomes.
Using Confirmatory Factor Analysis and
Cronbach’s alpha to assess the psychometric
properties of the scores on the MHRM-R, it was
concluded that the scores were both valid and
reliable. See Table 2 below.
Psychometric Properties of the Mental
Health Recovery Measure-Revised
Building the Model
• Level 1: The Unconditional Model:
MHRM_CENti = π0i + π1i*(TIME0ti) + eti
• Level 2: The Conditional Model:
π0i = β00 + β01*(AGEi) + β02*(GENDERi)
+ β03*(DIAGNOSIi) + r0i
π1i = β10 + β11*(AGEi) + β12*(GENDERi) +
β13*(DIAGNOSIi) + r1i
All statistical tests were conducted with α =.05.
• Research Question # 1: The scores from the target population had excellent internal consistency
and good model fit.
• Research Question # 2: Evidence supported growth (recovery) over time
Level 1: Unconditional Model:
This model only included the MHRM-R recovery scores as the outcome variable and TIME as an
independent variable. The unconditional model determined that there was indeed growth over time,
allowing the addition of Level 2 to the model, where possible explanatory variables were included
(Raudenbush & Bryk, 2002).
Mean Intercept: Estimations of the mean intercept, 𝛽00= -0.804, p = 0.482, were not significant
indicating that this parameter did not describe the average admission recovery score.
Mean Growth Trajectory: The mean growth rate, 𝛽10=1.06, p = 0.025, for the MHRM-R recovery
scores was significant, providing evidence that residents were gaining an average of 1.06 points
every three months to their MHRM-R recovery scores
Mean Growth Trajectories Individual Growth Trajectories
Results & Discussion
(1)
Level 2: Conditional Model: See Table 5
Findings from the unconditional model confirmed the requisite of a conditional
model with explanatory variables of personal characteristics (i.e., age, gender, and
primary diagnosis) added to the model, at Level 2:
• Research Question # 3: There was a significant difference in the growth
trajectory of recovery outcomes based on gender ( 𝐵11 = −1.13, 𝑝 = 0.02).
Gender was a significant explanatory variable for growth over time, providing
evidence that, regardless of diagnosis or recovery score upon admission, females
recovered faster than males.
• Research Question # 4: Baseline Scores: Scores on the MHRM-R differed
significantly based on primary diagnosis, ( 𝐵02 = 2.39, 𝑝 = .046) with individuals
presenting with schizophrenia scoring higher upon admission than those with
other diagnoses, suggesting lack of insight into their disorder (Young, 2003)
• Research Question # 5: :There was not a significant difference in recovery
outcomes based on the residents’ age for the intercept (i.e., where the residents’
started; p = 0.434, nor in their growth trajectories (i.e., the residents’ recovery over
time; p = 0..141)
Implications & Limitations
(2)
(3)
Females showed significant recovery over time
regardless of diagnosis or scores upon admission
Scores upon admission differed significantly based
on primary diagnosis
[a] [b] [c] [d] [e]
Some individuals
completed the MHRM-R
up to one month
following admission
and, therefore,
experienced the benefits
of treatment prior to
their first assessment
The MHRM-R is a self-
report measure, and
while the results show
significant growth in
recovery scores, they
are only based on the
treatment received at
CooperRiis
The growth model was
based on 12 months of
data. If additional data
had been available,
trajectories may or may
not have improved for
both males and females
Small effect size: Only
10.5% of the variance of
growth rates based on
gender was explained by
the model, suggesting
the need for additional
explanatory variables in
the model
Collapsing primary diagnosis
into only two categories may
have reduced the amount of
variance explained by the
model, severely limiting the
ability to detect true variance in
a multitude of diagnoses and
comorbidities
Table 2
Model Fit Indices MHRM-R (30 items) Acceptable Fit
Satorra-Bentler Scaled χ2 708.50, p < .0001 p value > .05
Non-Normed Fit Index (NNFI) 0.95 ≥ .95
Comparative Fit Index (CFI) 0.96 ≥ .95
Standardized RMR (SRMR) .063* ≤ .08
Root Mean Square Error of
Approximation (RMSEA)
0.084 < .06 to .08
Cronbachs α 0.941 > .80
Evidence of Validity and Reliability: Fit Indices and Cronbach's alpha for the MHRM-R
* According to Hu and Bentler (1999), when data is non-normal or n is small,
SRMR is preferred over RMSEA
Table 1
Years N Gender Primary Diagnosis
2003-2012 277 Female Schizophrenia 27 Range 18-71
Other Diagnoses 85 Mean 31.71
Male Schizophrenia 77 SD 10.61
Other Diagnoses 88
Participants Descriptive Statistics
122
155
Age (in years)n
![Modeling Mental Health Recovery:
From Psychometric Properties to Analysis of a Hierarchical Linear Growth Model
Karen Traxler, M.S., Suzy Landram, M.S., Tyler Kincaid, M.S., & Lisa Rue, Ph.D.
Applied Statistics and Research Methods
University of Northern Colorado
ASA: Women In Statistics; Raleigh Durham, North Carolina: May 15-17, 2014
Abstract Results & DiscussionMethods
Purpose
Participants
All participants were residents of CooperRiis Healing
Farm in North Carolina
Demographics
Data Analysis: 5 waves of data
• A two-level Hierarchical Linear Growth Curve
Model (HLM) was used to assess mental health
recovery over time. Five time periods (or waves of
data) were used: admission, 3 months, 6 months,
9 months, and 12 months.
• The strength of a HLM Growth Curve Model over
other regression models is that it can differentiate
between individual starting points (intercepts) as
well as individual change/recovery over time
(slopes) (Raudenbush & Bryk, 2002).
• Both HLM 7 and SPSS 20 (PASW, 2012) were used
for data analysis and the results were comparable
Waves of Data
Procedures
• Grand mean centering
was applied to an
individual’s MHRM-R
scores at each wave
point to simplify the
interpretation of scores
• Maximum Likelihood Estimation (MLE) was used
for all analysisBased on the RCC approach to mental health recovery, the purpose of this
study was to:
(a) Examine the psychometric properties of the Mental Health Recovery
Measure-Revised (MHRM-R) collected from a target population of
individuals with severe and persistent mental health conditions
(b) Assess whether mental health recovery outcomes, based on the MHRM-
R improved over time for the target population
(c) Investigate the roles age, gender, and/or primary diagnosis play in
positive mental health recovery outcomes over time
Target Population: Individuals with Severe and Persistent
Mental Health Conditions
Statistically modeling mental health recovery for individuals with severe
and persistent mental conditions has traditionally been accomplished
using the medical model of recovery which encompasses the elimination
or reduction of symptoms through medication and/or hospitalization. A
more holistic approach gaining support among both clinicians and
consumers is the Recovery-Centered Collaborative Approach to
mental health recovery (the RCC model) which is a person-centered
approach integrating medication with spirituality, hope, physical
wellbeing, life skills, strategies for managing symptoms, and strong
community and family support. Methods: Data from CooperRiis Healing
Farm, a residential treatment facility for individuals with severe and
persistent mental health conditions, specializing in the RCC approach
were examined using a Hierarchical Linear Growth Model (HLGM) to
assess recovery over a twelve month time period. Data included
variables such as age upon admission, gender, and primary diagnosis.
Results: The results from level 1 of the HLGM provided evidence that
there was significant positive growth in recovery scores over time.
Results from level 2 of the HLGM revealed that (1) individuals with
schizophrenia or other psychotic disorders had a significantly higher
recovery score upon admission than individuals with other diagnoses,
suggesting lack of insight into their disorder, (2) females recovered
significantly faster than males, regardless of age or primary diagnosis, (3)
individuals with a diagnosis of personality disorder or substance abuse
recovered ____ than individuals diagnosed with bipolar disorder,
depression, anxiety, attention deficit disorder, or schizophrenia and other
psychotic disorders.
Special thanks to:
Dr. Sharon Young &
Matt Snyder, M.S., M.A., L.P.C.
CooperRiis Healing Community
Special thanks to:
Dr. Susan Hutchinson
University of Northern Colorado
Research Questions
• Interactions: Interactions of independent variables were tested and no
significance was found
• Proportion of Variance Explained: The primary diagnosis of the residents (i.e.,
schizophrenic or not) explains 15.3% of the parameter variance in the initial status
(i.e., where a given resident baseline recovery score will start) and gender
accounts for 10.5% of the parameter variance in growth rates of recovery over
time.
• Final Estimates HLM Growth Model Gender & Primary Diagnosis
Recovery Over Time
Implications for Mental Health Researchers, Clinicians, & Consumers
Implications for Mental Health Researchers, Clinicians, and Consumers
• The MHRM-R is an good measure of mental health recovery and would be appropriate for
researchers and clinicians to utilize in the target population
• This study supports the holistic recovery-centered collaborative approach to recovery as a
viable alternative to the medical model of mental health recovery, even in patients with the
most severe and persistent mental health disorders
• Applied researchers and clinicians can use this information to develop appropriate person-
centered treatments for severe mental health conditions
• Individuals seeking mental health treatment for acute symptoms can have a voice in their
recovery process and maintain hope throughout their journey of symptom management.
Limitations
Recovery is a complex process involving far more than age, gender, and a dichotomized primary
diagnosis, therefore significant limitations are inherent in any explanatory model of recovery over time
• Research Question # 1: Is the MHRM-R a reliable and valid measure of
mental health recovery in the target population?
• Research Question # 2: Does mental health recovery of individuals with
severe and persistent mental health conditions, receiving a recovery-
centered collaborative mental health intervention, improve over time?
o Is there a difference in recovery outcomes over time based on:
• Research Question # 3: gender?
• Research Question # 4: primary diagnosis?
• Research Question # 5: age?
Instrumentation
Mental health recovery was measured using the
Mental Health Recovery Measure-Revised
(MHRM-R: Young & Ensing, 2003). The MHRM-R
is a 30-item self-report survey with a traditional
five-point Likert response scale ranging from 1=
strongly disagree to 5 = strongly agree. Higher
scores indicate more positive recovery outcomes.
Using Confirmatory Factor Analysis and
Cronbach’s alpha to assess the psychometric
properties of the scores on the MHRM-R, it was
concluded that the scores were both valid and
reliable. See Table 2 below.
Psychometric Properties of the Mental
Health Recovery Measure-Revised
Building the Model
• Level 1: The Unconditional Model:
MHRM_CENti = π0i + π1i*(TIME0ti) + eti
• Level 2: The Conditional Model:
π0i = β00 + β01*(AGEi) + β02*(GENDERi)
+ β03*(DIAGNOSIi) + r0i
π1i = β10 + β11*(AGEi) + β12*(GENDERi) +
β13*(DIAGNOSIi) + r1i
All statistical tests were conducted with α =.05.
• Research Question # 1: The scores from the target population had excellent internal consistency
and good model fit.
• Research Question # 2: Evidence supported growth (recovery) over time
Level 1: Unconditional Model:
This model only included the MHRM-R recovery scores as the outcome variable and TIME as an
independent variable. The unconditional model determined that there was indeed growth over time,
allowing the addition of Level 2 to the model, where possible explanatory variables were included
(Raudenbush & Bryk, 2002).
Mean Intercept: Estimations of the mean intercept, 𝛽00= -0.804, p = 0.482, were not significant
indicating that this parameter did not describe the average admission recovery score.
Mean Growth Trajectory: The mean growth rate, 𝛽10=1.06, p = 0.025, for the MHRM-R recovery
scores was significant, providing evidence that residents were gaining an average of 1.06 points
every three months to their MHRM-R recovery scores
Mean Growth Trajectories Individual Growth Trajectories
Results & Discussion
(1)
Level 2: Conditional Model: See Table 5
Findings from the unconditional model confirmed the requisite of a conditional
model with explanatory variables of personal characteristics (i.e., age, gender, and
primary diagnosis) added to the model, at Level 2:
• Research Question # 3: There was a significant difference in the growth
trajectory of recovery outcomes based on gender ( 𝐵11 = −1.13, 𝑝 = 0.02).
Gender was a significant explanatory variable for growth over time, providing
evidence that, regardless of diagnosis or recovery score upon admission, females
recovered faster than males.
• Research Question # 4: Baseline Scores: Scores on the MHRM-R differed
significantly based on primary diagnosis, ( 𝐵02 = 2.39, 𝑝 = .046) with individuals
presenting with schizophrenia scoring higher upon admission than those with
other diagnoses, suggesting lack of insight into their disorder (Young, 2003)
• Research Question # 5: :There was not a significant difference in recovery
outcomes based on the residents’ age for the intercept (i.e., where the residents’
started; p = 0.434, nor in their growth trajectories (i.e., the residents’ recovery over
time; p = 0..141)
Implications & Limitations
(2)
(3)
Females showed significant recovery over time
regardless of diagnosis or scores upon admission
Scores upon admission differed significantly based
on primary diagnosis
[a] [b] [c] [d] [e]
Some individuals
completed the MHRM-R
up to one month
following admission
and, therefore,
experienced the benefits
of treatment prior to
their first assessment
The MHRM-R is a self-
report measure, and
while the results show
significant growth in
recovery scores, they
are only based on the
treatment received at
CooperRiis
The growth model was
based on 12 months of
data. If additional data
had been available,
trajectories may or may
not have improved for
both males and females
Small effect size: Only
10.5% of the variance of
growth rates based on
gender was explained by
the model, suggesting
the need for additional
explanatory variables in
the model
Collapsing primary diagnosis
into only two categories may
have reduced the amount of
variance explained by the
model, severely limiting the
ability to detect true variance in
a multitude of diagnoses and
comorbidities
Table 2
Model Fit Indices MHRM-R (30 items) Acceptable Fit
Satorra-Bentler Scaled χ2 708.50, p < .0001 p value > .05
Non-Normed Fit Index (NNFI) 0.95 ≥ .95
Comparative Fit Index (CFI) 0.96 ≥ .95
Standardized RMR (SRMR) .063* ≤ .08
Root Mean Square Error of
Approximation (RMSEA)
0.084 < .06 to .08
Cronbachs α 0.941 > .80
Evidence of Validity and Reliability: Fit Indices and Cronbach's alpha for the MHRM-R
* According to Hu and Bentler (1999), when data is non-normal or n is small,
SRMR is preferred over RMSEA
Table 1
Years N Gender Primary Diagnosis
2003-2012 277 Female Schizophrenia 27 Range 18-71
Other Diagnoses 85 Mean 31.71
Male Schizophrenia 77 SD 10.61
Other Diagnoses 88
Participants Descriptive Statistics
122
155
Age (in years)n](https://image.slidesharecdn.com/07400b6e-8c80-4673-8e52-5e5824519a07-150220210810-conversion-gate02/85/AERA-2013_update1-1-320.jpg)
