Eating Habits of Paramedics Working in the City of Québec: A Pilot Study
Bibliographic record
Abstract
Background: Many studies have shown a possible association between working conditions and unhealthy behaviours that could lead to overweight and obesity.For paramedics, unpredictable lunch periods could lead to changes in eating behaviour during work.Objective: This study examines paramedics' eating behaviours in day and night shift workers in comparison with eating behaviours during one day off.Method: Twenty two paramedics, night (n=12) and day (n=10), shift workers recorded their energy intake and energy expenditure during one day-off and one day on duty.Energy intake was calculated using a self-reported dietary intake through three-day food record.Energy expenditure was calculated via indirect calorimetric method.The differences between day and night shift groups (between factors) and day on duty with day off conditions (within factor) were tested using two way ANOVA (2 groups *2 conditions) with age as covariate.Analyses were conducted using SPSS (v13.0,IBM, Ontario, Canada) software Results: Paramedics were 34 ± 11 years old, and 77% of paramedics presented overweight or obesity (BMI ≥ 25 kg/m²).Energy intake was significantly higher during the day off compared with the day on duty in both groups (F(1,19)= 17.07, p=0.001).Energetic balance was also higher during day off compared with the day on duty in both groups (F(1,19)=14.11,p=0.001).No statistically significant differences were found among shifts in energy intake, and energy expenditure between groups (respectively, F(1,19)=1.77,F(1,19)=3.43,all p>0.05), however energetic balance was significantly higher in night workers compared with day workers (F(1,19)=4.82,p=0.04). Conclusion:This study showed a reduction of energy intake during the day on duty and energetic imbalance between a day on duty and a day off.To help paramedics maintain energetic balance whatever their workload, emergency medical should pay attention to meal period availability and meal place.Also, preventive measures should target individual perceptions of health in paramedical service
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".