The Role of Resilience on Correctional Worker Wellbeing: A Systematic Review
Bibliographic record
Abstract
Introduction Correctional workers face uniquely stressful conditions that can impact their personal and professional wellbeing. Resilience, defined as the ability to adapt and thrive in adverse conditions, may be a key mitigator of occupational stress. Despite its potential benefits, few reviews examine the wellbeing of correctional workers (Miller, O., Bruenig, D., & Shakespeare-Finch, J. 2022; 49(11), 1559-1579) but have not comprehensively addressed resilience. Objectives 1. Describe resilience and summarize measures used to assess resilience in correctional settings. 2. Investigate the role of resilience on psychosocial wellbeing, burn-out, work performance, work leaves, attitudes, response to adverse incidents and turn-over among correctional workers. 3. Describe risk factors associated with resilience among correctional workers. 4. Describe study-defined strategies to build resiliency and relevant recommendations for future research and clinical practice. Methods The present review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Major databases (PubMed/MEDLINE, Embase, PsycINFO, Scopus, and CINAHL) were searched for eligible reports. At least two independent reviewers were responsible for screening and data collection. Conflicts were resolved via discussion, with input from senior authors when necessary. Quality appraisal was conducted for all included reports. Results As shown in Figure 1, a total of 679 articles were identified through major database searches. Title and abstract screening yielded 51 articles eligible for full-text review. The majority of articles were set in North American correctional facilities. Key factors for resilience included support systems, purpose, and optimism, while workplace adversity was a risk factor. Resilience was found to reduce symptoms of burnout and depression, and be crucial for managing psychosocial wellbeing. The presentation will discuss strategies to build resilience and highlight relevant recommendations. Image 1: Conclusions Overall, resilience was found to play a mediating role in the wellbeing of correctional workers. Further studies involving a standardized measure of resilience and broader populations and correctional settings are required to improve the validity and generalizability of findings. Disclosure of Interest None Declared
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".