“Cura te ipsum”: Healthy public safety leaders for healthy organizations
Bibliographic record
Abstract
Our objective was to complete a systematic review on the mental health and wellness of public safety service leaders. We worked to refine a search strategy that would enable us to identify material about the mental health of public safety leaders; we were left with tens of thousands of potential articles for review, with virtually no evidence of relevant material. In response, we outline emergent patterns through our efforts to synthesize the literature, drawing attention to the dominant areas of leadership research: leaders supporting, creating, and being responsible for a culture of mental health for their workforce, without themselves being seen as part of that workforce – people who also require support. We highlight the limited international scholarship tied to public safety leadership styles, responsibilities, and mental health, then draw attention to leadership needs, particularly the need for more research on public safety leaders given their isolation and the complex, liability-laced, political, and personally difficult space they occupy. We recommend future research and targeted intervention to preserve and even improve leadership health. Our impetus remains in how leaders too need support to have their own unique health needs met if they are to lead efforts that preserve the wellness of members and the functioning of their organization. Thus, they require tailored interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".