MétaCan
Menu
Back to cohort
Record W2808838775 · doi:10.1093/heapro/day040

The adoption, implementation and maintenance of a school food policy in the Canadian Arctic: a retrospective case study

2018· article· en· W2808838775 on OpenAlexafffundabout
Bonnie Fournier, Velma Illasiak, Kaysi Eastlick Kushner, Kim D. Raine

Bibliographic record

VenueHealth Promotion International · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of AlbertaThompson Rivers University
FundersCanadian Institutes of Health Research
KeywordsInfluencer marketingPsychological interventionFood policyHealth policyActive livingPolitical scienceBusinessPublic relationsMedicinePsychologyEnvironmental healthNursingPublic healthGeographyMarketingFood securityAgriculture

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.473
Teacher spread0.397 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueHealth Promotion InternationalSame topicIndigenous Studies and EcologyFrench-language works237,207