Quebec Serve and Protect Low Back Pain Study: What About Mental Quality of Life?
Bibliographic record
Abstract
As of now, the impact of low back pain (LBP) and its chronic state, chronic low back pain (CLBP), on mental health-related quality of life (HRQOL) has never been investigated among police officers. The present investigation aims at studying this relationship using a biopsychosocial model. Between May and October 2014, a Web-based cross-sectional study was conducted among Quebec police officers (Quebec, Canada). Mental HRQOL was measured using the role emotional (RE) and the mental health (MH) domains of the SF-12v2 Health Survey. The impact of CLBP on mental HRQOL (as opposed to acute/subacute LBP or no LBP) was studied with a multivariate linear regression model. Of the 3,589 police officers who participated in the study, 1,013 (28.4%) reported CLBP. The mean age of respondents was 38.5 ± 8.7 years, and 32.0% were females. The RE (44.1/100) and MH (49.0/100) mean scores of the CLBP group were comparable with the scores found in populations suffering from cancer or heart diseases. Compared to officers without LBP, the presence of CLBP was significantly associated with lower RE (β: −0.068; p = 0.003) and MH (β: −0.062; p = 0.002) scores. These relationships were not found in the acute/subacute LBP group. Our results underscore how frequent CLBP is among police officers and how burdensome it is. Considering the importance of good physical and mental health for this occupational population, police organizations should be aware of this issue and contribute to the efforts toward CLBP prevention and management in the workplace.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".