Eating Patterns among Emergency Medical Service Providers in the United States: A Qualitative Interview Study
Bibliographic record
Abstract
Emergency medical service (EMS) providers experience demanding work conditions in addition to shift work, which increases risk for nutrition related chronic disease such as metabolic syndrome, diabetes, obesity, and cardiovascular disease. The high stress, emergent, and unpredictable nature of EMS may interfere with healthy eating patterns on and off shift, however little is known about how these conditions impact dietary patterns among EMS providers. This study aimed to understand factors impacting dietary patterns through semi-structured interviews with 40 EMS providers throughout the United States. Interviews were conducted virtually via Zoom video conference. Inductive coding was used to identify themes throughout the interviews. Salient factors mentioned in the interviews included hunger, fatigue, stress, coworker influence, ambulance posting, geographical location, agency policy, and culture. Factors were grouped into 4 domains: physiological factors, psychosocial factors, physical environment, and organizational environment, represented by an adapted version of the social ecological model of health behaviors to include factors influencing eating patterns specific to EMS, which may contribute to overall health. Various barriers to healthy eating exist within EMS, and future studies should explore interventions at each level of our proposed model to improve conditions and reduce nutrition related disease risk in this essential population.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".