Occupational and Ergonomic Factors Associated With Low Back Pain Among Car-patrol Police Officers
Bibliographic record
Abstract
OBJECTIVES: Low back pain (LBP) is frequent and burdensome among police officers, but occupational and ergonomic factors associated with LBP and its chronic symptoms have never been studied among these workers using a biopsychosocial model. This study aimed at exploring such factors associated with acute or subacute LBP and chronic low back pain (CLBP) among car-patrol police officers. METHODS: A web-based cross-sectional study was conducted among car-patrol officers working in the province of Quebec (Canada). Factors associated with acute or subacute LBP and CLBP (as opposed to absence of LBP) were studied using a multivariate multinomial regression model. RESULTS: A total of 2208 car-patrol officers composed the study population. Statistically significant occupational/ergonomic determinant for higher prevalence of acute or subacute LBP was more frequent discomfort in the lower back when sitting in the patrol car as a driver (adjusted odds ratio [OR], 3.008; 95% confidence interval [CI], 2.170-4.168). More frequent posttraumatic interventions was associated with lower prevalence of acute or subacute LBP (adjusted OR, 0.609; 95% CI, 0.410-0.907). Occupational and ergonomic factors associated with higher prevalence of CLBP were greater seniority (adjusted OR, 1.061; 95% CI, 1.007-1.118) and more frequent discomfort in the lower back when sitting in the patrol car as a driver (adjusted OR, 7.546; 95% CI, 5.257-10.831). DISCUSSION: Few occupational and ergonomic factors that police organizations could use to better tailor prevention were found to be associated with acute or subacute LBP and CLBP. This cross-sectional study is an efficient first investigation for screening hypotheses that should be confirmed in further cohort studies.
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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 | 0.002 |
| 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 | 0.000 |
| 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".