How Do We Keep our Heads above Water? An Embedded Mixed-Methods Study Exploring Implementation of a Workplace Reintegration Program for Nurses Affected by Operational Stress Injury
Bibliographic record
Abstract
BACKGROUND: Nurses are exposed to potentially psychologically traumatic events which can lead to operational stress injuries (OSI). Workplace reintegration after an OSI can be challenging, especially with repeated exposure to potentially traumatic scenarios and workplace demands. A workplace reintegration program (RP) originally developed for police officers may be of benefit for nurses returning to work after an OSI. The purpose of this study is to investigate the perceived need for an RP for nurses, and its potential contextualization and implementation in the nursing context using an implementation science approach. METHODS: = 19). Data analysis was conducted using descriptive statistics, thematic analysis, and an organizational readiness assessment. RESULTS: Study participants indicated that formalized processes were rarely used to support nurses returning to work after time off due to mental health challenges. Themes included (1) "The Perfect Storm": the current state of return-to-work, (2) Integral Needs, and (3) A Break in the Clouds: hope for health. CONCLUSIONS: Exploration of innovative programs such as the RP may provide additional support to nurses affected by OSIs. Further research is needed regarding workplace reintegration for nurses, and contextualization and evaluation of the RP.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".