Mental health of public safety personnel: Developing a model of operational, organizational, and personal factors in public safety organizations
Bibliographic record
Abstract
The work of public safety personnel (PSP) such as police officers, firefighters, correctional officers, and paramedics, as well as other PSP, makes them vulnerable to psychological injuries, which can have profound impacts on their families and the communities they serve. A multitude of complex operational, organizational, and personal factors contribute to the mental health of PSP; however, to date the approach of the research community has been largely to explore the impacts of these factors separately or within single PSP professions. To date, PSP employers have predominantly focused on addressing the personal aspects of PSP mental health through resiliency and stress management interventions. However, the increasing number of psychological injuries among PSPs and the compounding stressors of the COVID-19 pandemic demonstrate a need for a new approach to the study of PSP mental health. The following paper discusses the importance of adopting a broader conceptual approach to the study of PSP mental health and proposes a novel model that highlights the need to consider the combined impacts of operational, organizational, and personal factors on PSP mental health. The TRi-Operational-Organizational-Personal Factor Model (TROOP) depicts these key factors as three large pieces of a larger puzzle that is PSP mental health. The TROOP gives working language for public safety organizations, leaders, and researchers to broadly consider the mental health impacts of public safety work.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".