School food programs and food insecurity at the REACH school network: an observational study
Bibliographic record
Abstract
BACKGROUND: Food insecurity is common in Canada and impacts children more than any other age group. This study aimed to evaluate the association between participation in school food programs and food insecurity among students attending Canada's largest urban school-based health centre program, the REACH School Network. METHODS: This is a cross-sectional observational study from April 2022 to June 2024 at the REACH School Network. We administered the Growth and Nutrition Questionnaire to parents of children aged 3-17 years. Questions were related to dietary intake and participation in school food programs. Our primary outcome was food insecurity, using the Hunger Vital Sign. Logistic regression estimated the association between regular school food program participation and food insecurity, adjusting for covariates. RESULTS: Of 477 eligible participants, 316 consented (66.2% response rate), and 223 were included in the analysis. The mean age was 9.21 years (SD = 3.08); 69.1% identified as male. Overall, 134 (60.1%) regularly participated in a school food program. Food insecurity was reported by 97 (43.5%) participants, with similar prevalence among participants (44.8%) and non-participants (41.6%). Logistic regression, both unadjusted (OR = 1.14; 95% CI 0.66-1.97; P = 0.637) and adjusted (OR = 0.82; 95% CI 0.42-1.63; P = 0.579) found no significant association between school food program participation and food insecurity. CONCLUSION: Our study highlights the complex relationship between food insecurity and school food program participation among an at-risk, urban children. Future research is needed, with larger sample sizes and longitudinal designs to better understand these complex relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".