Mental health and social support among public safety personnel
Bibliographic record
Abstract
BACKGROUND: Social support may be a protective factor for the mental health of public safety personnel (PSP), who are frequently exposed to potentially psychologically traumatic events and report substantial post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) symptoms. Research examining perceived social support and its association with PTSD and MDD in different PSP categories (e.g. firefighters, paramedics) is limited. AIMS: To examine differences in perceived social support across PSP and determine whether perceived social support is associated with differences in rates of MDD and PTSD. METHODS: We asked Canadian PSP, including correctional workers and officers, public safety communications officials, firefighters, paramedics, municipal and provincial police officers, and Royal Canadian Mounted Police (RCMP) officers, to complete an online anonymous survey that assessed socio-demographic information (e.g. occupation, sex, marital status, service years), social supports and symptoms of mental disorders, including PTSD and MDD. Analyses included ANOVA and logistic regression models. RESULTS: Perceived social support differed by PSP occupation. RCMP officers reported lower social support than all other PSP except paramedics. For most PSP categories, PSP who reported greater social support were less likely to screen positive for PTSD (adjusted odds ratios [AORs]: 0.90-0.93). Across all PSP categories, greater perceived social support was associated with a decreased likelihood of screening positive for MDD (AORs: 0.85-0.91). CONCLUSIONS: Perceived social support differs across some PSP categories and predicts PTSD and MDD diagnostic status. Studies involving diagnostic clinical interviews, longitudinal designs and social support interventions are needed to replicate and extend our results.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".