Workplace exposure to suicide among Australian mental health workers: A mixed‐methods study
Bibliographic record
Abstract
Workplace exposure to suicide attempts and deaths has been widely recognized as an occupational hazard for mental health and social care workers, including mental health nurses. Research consistently demonstrates the adverse impact on professionals. This paper explores the results of an online survey examining suicide exposure and impact. Of the 3010 Australian adult participants who identified exposure to suicide attempts and/or deaths in a larger study, 130 indicated that the most impactful suicide attempt and/or death exposure was that of a client or service user. While distress levels were relatively low among participants with workplace exposure, the qualitative content from 53 participants provides illumination into this experience. Themes that emerged in the qualitative responses include impact on the professional, organization response, and lack of adequate resources and supports to prevent suicide. Previous research has examined the impact of suicide exposure among professionals specifically, but this is the first known study of participants in a community sample who identified the most impactful suicide attempt or death exposure they had experienced was that of a client in a mental health setting. Workplace exposure among mental health workers is common and can have both deleterious and positive effects. Bereavement focused outcomes, where the loss of an attachment relationship is the focus, does not capture the full range of experiences in workplace exposure. Systemic issues in mental health care contribute to further distress among exposed workers, and this requires additional investigation and response.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".