Supporting public safety leaders: Applying empirical findings to the emerging evidence
Bibliographic record
Abstract
Following a review of the limited international evidence on the mental health and wellness of public safety leaders published in this journal, we wanted to present our findings on prison governors in the United Kingdom to see if our empirical data added further insight into this important area. Our research consisted of interviews with 63 prison governors (managers or leaders) in England, Scotland, and Wales where we aimed to explore their health and well-being. The interviews provided a wealth of data which helped us to explore how prison governors were feeling in relation to their physical and mental health, their work–life balance, and their feelings toward their role consequently exploring the impacts their work had in these areas. We mapped our findings to the points raised in the review of the evidence base to identify where our empirical findings provide support, or contradictions, to these. Our data supported the issues raised throughout the evidence review; it is imperative that public safety leaders have access to tailored, confidential support to help them stay well. Our recommendations align with those points made from the evidence base, specifically that peer support and reflective interventions could help to promote the well-being of public safety leaders, and that more research is needed into their health and well-being to develop the emerging evidence base and inform new approaches to support.
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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 | 0.142 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".