Nutrition and Exercise Environment Available to Outpatients, Visitors, and Staff in Children's Hospitals in Canada and the United States
Bibliographic record
Abstract
BACKGROUND: Children's hospitals should advocate for children's health by modeling optimum health environments. OBJECTIVES: To determine whether children's hospitals provide optimum health environments and to identify associated factors. DESIGN: Telephone survey. SETTING: Canadian and US hospitals with accredited pediatric residency programs. PARTICIPANTS: Food services directors or administrative dietitians. MAIN OUTCOME MEASURES: Health environment grades as determined for 4 domains quantifying (1) the amount of less nutritious food sold at cafeterias (cafeteria grade), (2) the presence of fast food outlets (outlet grade), (3) the amount of nutritious food alternatives available (healthful alternative grade), and (4) the presence of patient obesity or employee exercise programs (program grade). RESULTS: The overall response rate was 87%. Compared with Canadian hospitals, US hospitals had more food outlets (89% vs 50%) and more snack/beverage vending machines (median, 16 vs 12) (P = .001 for both), despite equivalent consumer numbers. External companies managed more outlets at US vs Canadian hospitals (65% vs 14%; P = .01), and, generally, US hospitals recuperated more revenue from their outlets. Worst cafeteria grade was associated with US hospital location (odds ratio [OR], 8.9; 95% confidence interval [CI], 1.6-50; P = .01) and lower healthful alternative grade (OR, 0.016; 95% CI, 0.002-0.15; P<.001). Lower grade in any domain was related to whether hospitals received more revenue from noncafeteria food outlets (OR, 1.7; 95% CI, 1.06-2.72; P = .03) and the presence of more internally operated cafeterias (OR, 2.3 per cafeteria; 95% CI, 1.53-3.36; P<.001). CONCLUSIONS: Children's hospitals provide suboptimal health environments. Reliance on revenue may be an important motivating factor encouraging the adoption of outlets that serve less nutritious food.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".