Fit for public safety: Informing attitudes and practices tied to the hiring of public safety personnel
Bibliographic record
Abstract
Recent recognition that public safety personnel (PSP) have a high prevalence of mental disorders has initiated a discussion around PSP mental readiness for their work. The discussions have raised new interest in potential protective factors or characteristics of PSP that may be identifiable at recruitment and used to mitigate mental disorders among high-exposure occupations. We draw on a pan-Canada study of mental disorder prevalence to understand the personal characteristics and factors that a sample of active PSP believe will impact the occupational success of recruits. We situate our work within the broader discussion of the expression of a shared responsibility between PSP recruits and PSP organizations, exploring how PSP perceive and describe hiring practices across public safety occupations. Our results indicate that accountability is currently placed on individual PSP to fully understand, in advance, the complexities and pressures inherent to their occupation. Accordingly, participants expressed a need for more scrupulous screening processes designed to recruit candidates who are ‘fit’ for the job, along with a belief that some recruits could be considered ‘unfit’ for employment, such as persons without an innate mental capability for PSP work. Cautions around unpacking the consequences versus ‘perceived’ need to properly screen individuals for their suitability as a PSP are discussed as well as the expressed co-responsibility of potential PSP and PSP organizations during hiring to learn about the job as a means to improve the mental health and wellbeing of the future PSP workforce.
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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".