Prevalence of current chronic pain in Royal Canadian Mounted Police cadets
Bibliographic record
Abstract
Background Nearly half of active duty Royal Canadian Mounted Police (RCMP) officers (i.e. 43%) report experiencing current chronic pain (i.e. pain lasting longer than 3 months). Most RCMP officers (i.e. 91%) who report chronic pain indicate that the pain started after working as RCMP officers. Baseline data on chronic pain prevalence among RCMP cadets has not been available.Aims The current study was designed to provide cross-sectional estimates of chronic pain prevalence among RCMP cadets starting the Cadet Training Program and to assess for sociodemographic differences among participants.Methods The RCMP Study uses a longitudinal prospective sequential experimental cohort design to create a clustered randomized trial that engages individual participants for 5.5 years. The current manuscript provides cross-sectional associations between chronic pain prevalence and sociodemographic characteristics. Participants were RCMP cadets (n=770) starting the Cadet Training Program. Location, intensity (i.e. on a 0-10 scale, and days per week experienced), and duration (i.e. number of months) of chronic pain was reported. Differences across sociodemographic characteristics were examined.Results Few RCMP cadets reported experiencing chronic pain (i.e. 10%); lower back pain was rated as the most severe in terms of intensity and duration, and second most frequently reported in number of days experienced per week. Prevalence of chronic pain was lower among RCMP cadets than RCMP officers.Conclusions Chronic pain prevalence among active duty RCMP officers may result from or be moderated by operational duties, as well as routine aging. Future researchers could examine ways to mitigate chronic pain development during RCMP officer careers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".