Driving the prevention of low back pain in police officers: A systematic review
Bibliographic record
Abstract
Introduction: Police officers spend a lot of time at work seated in a patrol car. Being seated for hours can cause low back pain (LBP) and is documented as sedentary behaviour, and the risk of work injuries related to LBP grows for police officers as a result of equipment and individual factors. This can result in an increase in health and safety costs for organizations and has a negative impact on quality of life. This systematic review 1) evaluated the risks associated with police patrolling while considering external factors that could influence a police officer's well-being and 2) identified solutions to improve posture while seated in vehicles and discerned preventive measures that can be implemented to alleviate LBP. Methods: were searched for articles published between January 1990 and December 2022. Results: Of 1,169 articles initially identified, 104 met the criteria for full-text review. Twenty-one articles specifically discussed LBP in police officers, vehicle ergonomics, training programs, and health habits. Discussion: To minimize LBP, analyzing optimal positions for the mobile data terminal, raising awareness among police officers (civilian and military), offering preventive or corrective training programs for trunk musculature, having multidisciplinary teams in organizations, and participating in regular physical activity could have positive effects on preventing LBP.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".