“I Would Have My Children Participate IF …”: Perceptions of Canadian Caregivers Towards School Food Programs
Bibliographic record
Abstract
Purpose: The Canadian federal government has expressed an intention to work with provinces and territories to develop a national school food program (SFP). This study aimed to explore caregivers’ perception of attributes important to include in a future SFP. Methods: An online cross-sectional survey was conducted. Fifteen elementary schools from high, medium, or low median income neighbourhoods in Saskatoon were invited to participate. School principals sent a survey link to students’ caregivers. The 37-item survey included an item with 15 statements asking caregivers to rate the importance of various components of a SFP. Descriptive statistics and exploratory factor analysis were conducted. Results: A total of 510 caregivers completed the survey (response rate of 52%). The factor analysis indicated four key components of a future SFP: (1) learning opportunities on growing and preparing food, (2) offering healthy food following Canada’s Food Guide, (3) affordability of the meals offered, and (4) cultural adaptability of the meal program. Over 90% of caregivers thought providing healthy meals and ample time to eat meals to be very important. Conclusion: Our results indicate caregivers support multicomponent meal programs that, along with providing nutritious food, help children build healthy habits and sustainable food systems. These findings will help dietitians understand caregivers’ perspectives to inform the design of a national SFP.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".