“Collapsing into Darkness”: An Exploratory Qualitative Thematic Analysis of the Experience of Workplace Reintegration among Nurses with Operational Stress Injuries
Bibliographic record
Abstract
Background: Nurses are engaged in an unpredictable and dynamic work environment where they are exposed to events that may cause or contribute to physical and/or psychological injuries. Operational stress injury (OSI) may lead to an extended time away from work or nurses leaving the profession altogether. A deliberate focus on the workplace reintegration phase of the mental health recovery process may lead to the increased retention of nurses in their profession. Prior to the creation and implementation of potential solutions to address workplace reintegration, it is imperative to explore the experiences and perceptions of nurses affected by OSI. This qualitative study aims to investigate the experiences and perceptions of nurses (N = 7) employed within a Canadian provincial healthcare system who have attempted workplace reintegration after being off of work with an OSI. Methods: Nurses were recruited via social media, unit emails, and word of mouth. Data were collected through recorded semi-structured interviews conducted over videoconferencing. Once transcribed, the data were thematically analyzed using an inductive approach. Results: The resulting themes included (1) heroes to zeros, (2) changing the status quo, (3) connection is key, and (4) post-traumatic growth: advocacy and altruism. Study participants indicated both that nursing culture and a cumulation of events contributed to a need for a leave of absence from work and that a formalized process was desired by nurses to assist in returning to work. Conclusions: The development, implementation, and exploration of innovative policies, procedures, and initiatives to bridge the gap from clinical interventions to workplace reintegration are needed for nurses experiencing OSI. Further research is also needed regarding mental health impacts and appropriate resources to support nurses in their workplace reintegration process after experiencing psychological and/or physical injury.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".