The adoption, implementation and maintenance of a school food policy in the Canadian Arctic: a retrospective case study
Bibliographic record
Abstract
With increasing childhood obesity rates and type 2 diabetes developing in younger age groups, many schools have initiated policies to support healthy eating and active living. Policy interventions can influence not only health behaviours in students but can also impact these behaviours beyond the school walls into the community. We articulate a policy story that emerged during the data collection phase of a study focused on building knowledge and capacity to support healthy eating and active living policy options in a small hamlet located in the Canadian Arctic. The policy processes of a local school food policy to address unhealthy eating are discussed. Through 14 interviews, decision makers, policy influencers and health practitioners described a policy process, retrospectively, including facilitators and barriers to adopting and implementing policy. A number of key activities facilitated the successful policy implementation process and the building of a critical mass to support healthy eating and active living in the community. A key contextual factor in school food policies in the Arctic is the influence of traditional (country) foods. This study is the first to provide an in-depth examination of the implementation of a food policy in a Canadian Arctic school. Recommendations are offered to inform intervention research and guide a food policy implementation process in a school environment facing similar issues.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".