Physical training in the fire station and firefighters’ cardiovascular health
Bibliographic record
Abstract
BACKGROUND: Few fire departments in Québec have a diversified health promotion programme. Yet, many allow firefighters to physically train during working hours. AIMS: To compare the weekly physical activity (PA) level and cardiovascular health indicators of firefighters who physically train on duty to those who do not. METHODS: Participants underwent a cardiovascular health assessment and completed an online questionnaire. RESULTS: One hundred and five full-time male firefighters participated in the study. Two groups were formed: firefighters who physically train while on duty (E, n = 64) and firefighters who do not (NoE, n = 41). Following statistical adjustments, off-duty weekly PA was not different between the two groups (E: 239 ± 224 versus NoE: 269 ± 249 min, P = 0.496); however, total weekly PA was higher (P = 0.035) in E (381 ± 288 min) than in NoE (274 ± 200 min). A difference was also observed in obesity prevalence measured with waist circumference (E: 9% versus NoE: 27%, P = 0.026) and in physical inactivity prevalence (E: 0% versus NoE: 27%, P < 0.001). After statistical adjustments, E firefighters have a significantly lower waist-to-height ratio than NoE firefighters (E: 0.51 ± 0.05 versus NoE: 0.54 ± 0.05, P = 0.017). CONCLUSIONS: Results show that firefighters who physically train while on duty have a higher total PA level on a weekly basis and have better cardiovascular health indicators. Our findings suggest that fire services should promote physical training while on duty to improve firefighters' cardiovascular health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".