Workplace Reintegration Programs, Policies, and Procedures for Nurses Experiencing Operational Stress Injury: A Scoping Literature Review
Bibliographic record
Abstract
BACKGROUND: Nurses experience elevated rates of operational stress injuries (OSIs). This can necessitate taking leave from work and subsequently engaging in a workplace reintegration process. An unsuccessful process can have long term impacts on a nurse's career, affecting the individual, their family, and broader community, while contributing to nursing shortages. A knowledge gap regarding the workplace reintegration of nurses experiencing mental health challenges, impedes the development and implementation of initiatives that might increase the success of nurses reintegrating into the workplace. This scoping review explored the existing literature concerning workplace reintegration for nurses experiencing OSIs. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines were utilized. Three key search terms across six databases were employed followed by a qualitative content analysis of the resulting literature. RESULTS: Eight documents were included. The literature exhibited high heterogeneity in objectives, content, and article types. The content analysis revealed five themes: (1) recognizing stigma, (2) elements of successful workplace reintegration, (3) considerations for military nurses, (4) considerations for nurses with substance use disorders, and (5) gaps in the existing literature. CONCLUSION: A paucity of programs, policies, procedures, and research exists regarding workplace reintegration for nurses facing mental health challenges. It is imperative to recognize that nurses may experience OSIs, necessitating mental health support, time off work, and operationally/culturally-specific assistance in returning to work. Innovative and evidence-based approaches to workplace reintegration are needed to enhance the retention of a skilled, experienced, compassionate, and healthy nursing workforce.
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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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".