Cardiovascular disease risk in female firefighters
Bibliographic record
Abstract
BACKGROUND: Female firefighters are in the minority in the Québec firefighter population and worldwide. To our knowledge, no study has focused on cardiovascular risk factors in female firefighters, and further research in this area is needed to evaluate and reduce the risk of on-duty sudden cardiac death. AIMS: To evaluate the prevalence of cardiovascular disease (CVD) risk factors in female firefighters in Québec. METHODS: A cross-sectional study using an online questionnaire to evaluate lifestyle and CVD risk factors and symptoms. RESULTS: Forty-one female firefighters (age: 38.2 ± 9.9 years) participated in this study, representing ~7% of all female Québec firefighters. The prevalence of obesity (body mass index ≥ 30 kg/m2), hypertension, dyslipidaemia, type 2 diabetes mellitus, smoking and physical inactivity was 12% (95% confidence interval [CI] 4-26%), 5% (95% CI 0.6-19%), 5% (95% CI 0.6-19%), 3% (95% CI 0.1-14%), 14% (95% CI 5-29%) and 62% (95% CI 5-7%), respectively. Among survey participants, 76% (59-88%) had moderate to high CVD risk according to the 2013 American College of Sports Medicine guidelines. Eighty-two per cent of participants did not meet the National Fire Protection Association's required cardiorespiratory fitness standard of 12 metabolic equivalents. CONCLUSIONS: A high proportion of female firefighters in this study were at moderate to high risk of CVD. These findings suggest that they would benefit from healthy lifestyle initiatives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".