Organizational Factors and Their Impact on Mental Health in Public Safety Organizations
Bibliographic record
Abstract
Public safety personnel (PSP), including correctional officers, firefighters, paramedics, and police officers, have higher rates of mental health conditions than other types of workers. This scoping review maps the impact of organizational factors on PSP mental health, reviewing applicable English language primary studies from 2000–2021. JBI methodology for scoping reviews was followed. After screening, 97 primary studies remained for analysis. Police officers (n = 48) were the most frequent population studied. Correctional officers (n = 27) and paramedics (n = 27) were the second most frequently identified population, followed by career firefighters (n = 20). Lack of supervisor support was the most frequently cited negative organizational factor (n = 23), followed by negative workplace culture (n = 21), and lack of co-worker support (n = 14). Co-worker support (n = 10) was the most frequently identified positive organizational factor, followed by supervisor support (n = 8) and positive workplace culture (n = 5). This scoping review is the first to map organizational factors and their impact on PSP mental health across public safety organizations. The results of this review can inform discussions related to organizational factors, and their relationship to operational and personal factors, to assist in considering which factors are the most impactful on mental health, and which are most amenable to change.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".