<i>Eat Smart! Workplace Cafeteria Program</i> Evaluation of the Nutrition Component
Bibliographic record
Abstract
PURPOSE: The nutrition component of the Eat Smart! Workplace Cafeteria Program (ESWCP) in a hospital was evaluated. We assessed staff's frequency of visits to and purchases in the hospital cafeteria, attitudes about the program, short-term eating behaviour change, and suggestions to improve the ESWCP. METHODS: Questionnaires were sent to hospital staff members who were not on leave (n=504). Dillman's Tailored Design Method was used to design and implement the survey. Four mail-outs were used and yielded a 51% response rate. RESULTS: Eighty-seven percent of respondents visited the hospital cafeteria at least once a week in an average seven-day week, and 69% purchased one to five meals or snacks there each week. Eighty-six percent of respondents said that they were aware of the hospital's program. Notices on cafeteria tables were the primary method of learning about the program (67%). Reported program benefits included increased knowledge about healthy eating, convenience of having healthy foods in the cafeteria, and increased energy. CONCLUSION: Many respondents were aware of the program, provided positive comments about it, and reported positive changes in eating habits. However, future observational research is warranted to note foods served and sold before and after program implementation, as well as to examine whether results can be generalized to other settings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".