Development and Validation of an Instrument to Measure Staff Perceptions of Recovery Climate and Culture in Mental Health Programs

Evans, Leigh; Wewiorski, Nancy J.; Ellison, Marsha Langer; Ni, Pengsheng; Harvey, Kimberly; Hunt, Marcia G.; Gorman, Jay A.; Charns, Martin P.

doi:10.1176/appi.ps.201900181

Cited by 4 publications

(5 citation statements)

References 30 publications

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“…Most of the classifications were based on the extent (or, say, strata) to which MI or other disorders are determined by summation scores or equivalent measures of a scale (eg, patients with a psychotic disorder [ 21 ] or those with 2 or 3 groups of nurse burnouts [or bullied victims] at the workplace [ 19 , 20 ]). To the best of our knowledge, none of the studies have applied the response pattern to classify the features of MI (or other disorders) with CNN modules to highlight less confident cases of MI characterized by the aberrant response pattern that deviated from normal cases.…”

Section: Introductionmentioning

confidence: 99%

An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study

Yan

Chien

Yeh

et al. 2020

JMIR Mhealth Uhealth

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Background Mental illness (MI) is common among those who work in health care settings. Whether MI is related to employees’ mental status at work is yet to be determined. An MI app is developed and proposed to help employees assess their mental status in the hope of detecting MI at an earlier stage. Objective This study aims to build a model using convolutional neural networks (CNNs) and fit statistics based on 2 aspects of measures and outfit mean square errors for the automatic detection and classification of personal MI at the workplace using the emotional labor and mental health (ELMH) questionnaire, so as to equip the staff in assessing and understanding their own mental status with an app on their mobile device. Methods We recruited 352 respiratory therapists (RTs) working in Taiwan medical centers and regional hospitals to fill out the 44-item ELMH questionnaire in March 2019. The exploratory factor analysis (EFA), Rasch analysis, and CNN were used as unsupervised and supervised learnings for (1) dividing RTs into 4 classes (ie, MI, false MI, health, and false health) and (2) building an ELMH predictive model to estimate 108 parameters of the CNN model. We calculated the prediction accuracy rate and created an app for classifying MI for RTs at the workplace as a web-based assessment. Results We observed that (1) 8 domains in ELMH were retained by EFA, (2) 4 types of mental health (n=6, 63, 265, and 18 located in 4 quadrants) were classified using the Rasch analysis, (3) the 44-item model yields a higher accuracy rate (0.92), and (4) an MI app available for RTs predicting MI was successfully developed and demonstrated in this study. Conclusions The 44-item model with 108 parameters was estimated by using CNN to improve the accuracy of mental health for RTs. An MI app developed to help RTs self-detect work-related MI at an early stage should be made more available and viable in the future.

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Section: Introductionmentioning

confidence: 99%

An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study

Yan

Chien

Yeh

et al. 2020

JMIR Mhealth Uhealth

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“…There is a lack of instruments that incorporate both multidimensional and evidencebased conceptualizations in culture theory in the areas of health (Evans et al, 2020) and risk research (Schmidt et al, 2020). To our knowledge, there are no established risk culture measures or survey methods for health-related risk culture.…”

Section: Measure Developmentmentioning

confidence: 99%

The structure of the Risk Culture Framework: Evidence from a health context

Bielefeld¹,

Streicher²,

Gürer³

2023

Preprint

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Risk assessment and behavior of social entities, such as societies, organizations or groups, are shaped by shared values and beliefs. Such shared convictions on how risk is perceived and handled are widely labeled as risk culture. While risk culture is a promising approach for comprehensively considering risk aspects in social dynamics, its structure still lacks conceptual clarity. In this regard, the recently introduced Risk Culture Framework (RCF) was aimed at providing an operationalization foundation for risk culture research through a 3x3 grid representing different cultural levels and influencing domains. However, until now, the RCF has neither been empirically applied nor tested. In the current study, the structural fit of the model is evaluated using empirical data pertaining to health risks gathered by an exploratory questionnaire (N = 500). For the sake of methodological consistency, the cultural level of implicit factors was not considered due to its methodological specificity. Confirmatory factor analyses were used to analyze the fit of the assumed model structure as well as that of other applicable models. Model indices for the RCF-oriented risk culture model structure were acceptable and better than those for the other models tested. Overall, results support the theoretical-based structure of the RCF, and provide a foundation for further research on risk culture. Future approaches and applications of the RCF to more specific risk subjects are discussed.

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“…It was designed to assess recovery promotion in seven organizational domains (staff expectations, staff values, program leadership, staff training, staff rewards, program policies, and quality improvement) and to incorporate 13 key recovery principles (individualized/person-centered, empowerment, hope, self-direction, relational, nonlinear/many pathways, strengths-based, respect, responsibility, peer support, holistic, culture-informed, and trauma-informed; Ellison et al, 2018). Results are forthcoming in a separate article (Evans et al, 2019).…”

mentioning

confidence: 95%

“…To address this, Evans and colleagues (2019) developed a staff self-report survey measure of recovery promotion in PRRCs. Using a two-dimensional array of key recovery principles and key organizational domains, they selected and modified items from 15 existing measures of recovery (Armstrong & Steffen, 2009; Bedregal, O’Connell, & Davidson, 2006; Burgess, Pirkis, Coombs, & Rosen, 2011; Campbell-Orde, Chamberlin, Carpenter, & Leff, 2005; Crowe, Deane, Oades, Caputi, & Morland, 2006; Dumont et al, 2006; Ellis & King, 2003; Higgins, 2008; Mancini, 2006; O’Connell et al, 2005; Oades, Crowe, & Deane, 2007; Ragins, 2010; Ridgway & Press, 2004; Russinova, Rogers, Ellison, & Lyass, 2011; Williams et al, 2015) and developed new items to capture recovery principles and organizational domains that were underrepresented in existing measures.…”

mentioning

confidence: 99%

A site visit protocol for assessing recovery promotion at the program level: An example from the Veterans Health Administration.

Wewiorski¹,

Gorman

Ellison

et al. 2019

Psychiatric Rehabilitation Journal

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Objective: A site visit protocol was developed to assess recovery promotion in the organizational climate and culture of programs for veterans with serious mental illnesses. Method: The protocol was pilot-tested in 4 programs: 2 that had scored high on the pilot version of a staff survey measure of program-level recovery promotion and 2 that had scored low. Two-person teams conducted onsite visits and assigned global and organizational domain ratings. Interrater agreement was assessed by examining adjacent agreement and computing weighted kappa. Results: The on-site protocol had good interrater agreement and discriminated between sites that scored high and low on the staff survey. Conclusions and Implications for Practice: This site visit protocol and procedure shows promise for evaluating recovery promotion in milieu-based programs. After further refinement of this tool, adaptations could be developed for accreditation protocols or for program self-assessment and quality improvement efforts. Impact and ImplicationsSite visits were conducted to evaluate the extent to which four programs had structures and cultures that promote mental health recovery. We developed an assessment tool that was able to differentiate between programs that were most likely and least likely to promote recovery. After further refinement, this assessment tool could be used in program accreditation and licensing reviews and in-house program quality improvement initiatives.

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Development and Validation of an Instrument to Measure Staff Perceptions of Recovery Climate and Culture in Mental Health Programs

Cited by 4 publications

References 30 publications

An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study

An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study

The structure of the Risk Culture Framework: Evidence from a health context

A site visit protocol for assessing recovery promotion at the program level: An example from the Veterans Health Administration.

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