Associations between Trauma Exposure and Physical Conditions among Public Safety Personnel: Associations entre l’exposition à un traumatisme et les problèmes physiques chez le personnel de la santé publique
Bibliographic record
Abstract
Background: Trauma exposure is associated with adverse health-related correlates, including physical comorbidities, and is highly prevalent among public safety personnel (PSP). The current study (1) examined the association between context of index trauma exposure (part of job vs. other) and physical conditions and (2) established the prevalence of physical conditions according to PSP category (e.g., police, paramedic) and index trauma type (e.g., serious accident, physical assault) in a large Canadian sample of PSP. Methods: PSP completed an online survey between September 2016 and January 2017. Multivariable logistic regressions examined associations between context of index trauma exposure (i.e., part of job vs. other) and physical condition categories. Cross-tabulations with chi-square analyses examined whether the prevalence of physical conditions significantly differed according to PSP category and index trauma type. Results: There were 5,267 PSP included in the current study. Results from the most stringent model of logistic regressions demonstrated that, compared to PSP who experienced their index trauma in any other context, PSP who experienced it as part of their job had reduced odds of “other” physical conditions (adjusted odds ratio = 0.73, 95% confidence interval, 0.57 to 0.94, P < 0.05). Results also revealed significant differences in the prevalence of physical conditions across all PSP categories and select index trauma types. Conclusion: Results highlight the relevance of trauma exposure outside of an occupational context among PSP and may have implications for the positive impact of stress inoculation and resiliency training programs for PSP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".