Chronic pain among public safety personnel in Canada
Bibliographic record
Abstract
Background: Chronic pain is highly prevalent in the general population and may be even higher among public safety personnel (PSP; e.g., correctional officers, dispatchers, firefighters, paramedics, police). Comprehensive data on chronic pain among diverse Canadian PSP are relatively sparse.Aims: The current study was designed to provide initial estimates of chronic pain frequency and severity among Canadian PSP.Methods: Estimates of chronic pain frequency and severity (i.e., intensity and duration) at different bodily locations were derived from self-reported data collected through an online survey. Participants included 5093 PSP (32.5% women) grouped into six categories (i.e., call center operators/dispatchers, correctional officers, firefighters, municipal/provincial police, paramedics, Royal Canadian Mounted Police [RCMP]).Results: Substantial proportions of participants reported chronic pain, with estimates ranging from 35.3% to 45.4% across the diverse PSP categories. Across PSP categories, chronic lower back pain was the most prevalent. For some pain locations, firefighters and municipal/provincial police reported lower prevalence, but paramedics reported lower intensity, and duration, than some other PSP groups. Over 50% of RCMP and paramedics reporting chronic pain indicated that the pain was associated with an injury related to active duty.Conclusions: Discrepancies emerged across PSP members with respect to prevalence, location, and severity. The current data suggest that additional resources and research are necessary to mitigate the development and maintenance of distressing or disabling chronic pain for Canadian PSP.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".