Mapping the peer-reviewed literature on accommodating nurses’ return to work after leaves of absence for mental health issues: a scoping review
Bibliographic record
Abstract
BACKGROUND: The complexity of nursing practice increases the risk of nurses suffering from mental health issues, such as substance use disorders, anxiety, burnout, depression, and posttraumatic stress disorder (PTSD). These mental health issues can potentially lead to nurses taking leaves of absence and may require accommodations for their return to work. The purpose of this review was to map key themes in the peer-reviewed literature about accommodations for nurses' return to work following leaves of absence for mental health issues. METHODS: A six-step methodological framework for scoping reviews was used to summarize the amount, types, sources, and distribution of the literature. The academic literature was searched through nine electronic databases. Electronic charts were used to extract code and collate the data. Findings were derived inductively and summarized thematically and numerically. RESULTS: Academic literature is scarce regarding interventions for nurses' return to work following leaves of absence for mental health issues, and most focused on substance use concerns. Search of the peer-reviewed literature yielded only six records. The records were primarily quantitative studies (n = 4, 68%), published between 1997 and 2018, and originated in the United States (n = 6, 100%). The qualitative thematic findings addressed three major themes: alternative to discipline programs (ADPs), peer support, and return to work policies, procedures, and practices. CONCLUSIONS: While the literature supports alternative to discipline programs as a primary accommodation supporting return to work of nurses, more on the effectiveness of such programs is required. Empirical evidence is necessary to develop, maintain, and refine much needed return to work accommodations for nurses after leaves of absence for mental health issues.
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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 | 0.068 | 0.274 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.083 | 0.059 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".