A Prospective Study Of Health-related Lifestyle Changes Among Police Recruits.
Bibliographic record
Abstract
PURPOSE: Several studies have indicated that law enforcement officers (LEOs) are at increased risk of cardiovascular morbidity and mortality. Behavioral risk factors could partly explain the disparity in cardiovascular risk as LEOs are known to generally display poor health-related lifestyles. Therefore, this study aimed to explore the health-related lifestyle of police cadets and to assess changes in the health behaviors of police recruits upon entering the police forces. METHODS: A survey-based prospective study was conducted to meet our objectives. A total of 190 police cadets completed an online questionnaire assessing their leisure-time physical activity level, diet quality, sleep hygiene, alcohol consumption, cigarette smoking, and stress level. One year following their graduation from the basic police training program, participants were invited to complete the questionnaire once again. RESULTS: Our results indicate that police cadets generally display healthy lifestyles with very few cadets being physically inactive (6.3%), cigarette smokers (6.3%), reporting insufficient sleep duration (16.1%), and being categorized as obese based on BMI (9.5%). However, paired-sample comparisons highlighted significant decreases in the weekly minutes of leisure-time physical activity (p < 0.01), fruit and vegetable intake (p < 0.01), sleep duration (p < 0.01), and sleep quality (p = 0.03) at the follow-up. Likewise, significant increases in fast-food consumption (p < 0.01) and BMI (p < 0.01) were observed. CONCLUSIONS: Overall, our results emphasize the importance of promoting healthy behaviors among LEOs during the early stage of their career, a period often marked by long working hours and frequent night shifts.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".