Effects of patient deaths by suicide on clinicians working in mental health: A survey
Bibliographic record
Abstract
In the UK, at least a quarter of suicides occurs in patients whilst under the care of mental health services. This study investigated the effects of such deaths on non-medical mental health clinicians. An online survey was conducted within a single NHS mental health Trust to elicit both quantitative and qualitative responses from staff across a range of professions. The survey focused on personal and professional impacts and available support. Participants reported significant negative emotional and professional effects that were long-lasting for some. These included mental health difficulties, loss of confidence regarding clinical responsibilities, and actual or contemplated career change. However, there was also some evidence of positive effects and professional growth. Support from colleagues and line managers is clearly important following deaths of patients by suicide. Clinicians' experiences of the support they had received in the workplace were polarized, suggesting that there is no single nor ideal approach that will meet everyone's needs. Participants made recommendations for the types of support that may be helpful. Most commonly, clinicians desired opportunities for focused reflection and support and help with the formal processes following the death. Sensitivity around how clinicians are notified about the death was highlighted as being particularly important. Conclusions are drawn as to how training institutions and employers can help staff to be better prepared for the potential occurrence of patient suicides and the formal processes that follow, with a view to mitigating risks of more serious harm to staff and hence indirectly to patients, and potential loss of highly trained clinicians to the 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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".