Alexander First Nations Parents’ Perceptions of a School Nutrition Policy
Bibliographic record
Abstract
Purpose: A school nutrition policy (SNP) is one promising school-based health promotion strategy to improve the food environments of First Nations children. The aim of this study was to explore First Nations parents’ perceptions of a SNP. Methods: A process evaluation of policy implementation was conducted using a mixed-methods design. Parents (n = 83) completed a 19-question survey to capture their perceptions of the policy. Survey responses informed questions in an 11-question semi-structured interview guide. Transcripts from interviews with parents (n = 10) were analyzed using content analysis to identify barriers and facilitators to policy implementation. Results: Parents were supportive of the SNP and the school’s food programs, which they perceived as helping to address community concerns related to nutrition. However, some parents opposed the restriction of unhealthy foods at school celebrations and fundraisers. In addition, despite being aware of the SNP, parents were unable to demonstrate an understanding of the SNP content. Finally, parents struggled to provide their children with healthy foods to bring to school due to lack of affordable and accessible food in the community. Conclusions: Although SNPs may be well-received in First Nations communities, their implementation must be supported by parent involvement and consideration of wider socioeconomic conditions.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".