Quebec Serve and Protect Low Back Pain Study
Bibliographic record
Abstract
STUDY DESIGN: Web-based cross-sectional study. OBJECTIVE: The aim of this study was to describe the prevalence and the burden of low back pain (LBP) and chronic low back pain (CLBP) among Quebec police officers. SUMMARY OF BACKGROUND DATA: Police officers have work-related factors associated with LBP, but chronicity and impacts of this condition have been little explored among this population. METHODS: Between May and October 2014, a web-based cross-sectional study was conducted among police officers working in the province of Quebec (Canada). Nine police organizations accepted to disseminate the email invitation to their members. The survey included the French-Canadian version of the Nordic Musculoskeletal Questionnaire and other items regarding functional impact of LBP and associated treatments. RESULTS: A total of 3589 police officers completed the questionnaire. Mean age was 38.5 ± 8.7 years, 32.0% were women, and 67.4% reported being car-patrol officers. A majority reported LBP symptoms in the past 12 months (67.7%) and 96.5% of them perceived that presence of LBP was totally/partially linked to their work in the police force. Prevalence of CLBP among all responders was 28.7%. Police officers reporting CLBP, as compared to those reporting acute or subacute LBP symptoms in the past 12 months, were more likely to report LBP-related reduction of work activities (64.4% vs. 45.7%; P < 0.001) and more working days lost in the past 12 months (average of 11.9 ± 43.5 vs. 1.5 ± 9.8; P < 0.001). A greater proportion also reported LBP-related health care visits in the past 12 months (86.2% vs. 64.2%; P < 0.001) and current use of pain medications/complementary alternative medicines (90.1% vs. 69.7%; P < 0.001). CONCLUSION: CLBP is a frequent and burdensome condition among Quebec police officers. Our results underline the importance for police organizations to promote CLBP prevention and to implement workplace management programs. LEVEL OF EVIDENCE: 3.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".