School Breakfast‐Club Program Changes and Youth Eating Breakfast During the School Week in the<scp>COMPASS</scp>Study
Bibliographic record
Abstract
BACKGROUND: Despite the importance of breakfast consumption, breakfast skipping is common among Canadian youth. This study examines how changes to school-based breakfast programs are associated with breakfast-skipping behavior. METHODS: Using school-level longitudinal data from Year 1 (Y1 : 2012-2013) and Year 2 (Y2 : 2013-2014) of the COMPASS study, quasi-experimental methods evaluated the impact over time that changes to school-based breakfast programs had on breakfast skipping or participating in school-based breakfast program. RESULTS: Between Y1 and Y2 , the school-level prevalence of breakfast skipping (54.5%-54.9%) and breakfast program participation (12.3%-13.6%) increased. Of the 43 participating schools, 5 implemented a new school breakfast program. Among the intervention schools, 1 school (School 4) observed a significant, and 1 school (School 3) observed a significant increase in the school-level prevalence of skipping breakfast; there was no significant change in the other 3 intervention schools. CONCLUSIONS: Despite the availability of free school breakfast programs, the majority of youth skipped breakfast at least once a school week. Owing to the variation in the types of programs implemented, additional evaluation evidence is necessary to determine which students benefited the most from these programs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".