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Record W2177653667 · doi:10.3148/70.4.2009.166

<i>Developing School Nutrition Policies:</i> Enabling and Barrier Factors

2009· article· en· W2177653667 on OpenAlexaffvenue
Debbie MacLellan, Jennifer Taylor, Catherine Freeze

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2009
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsGovernment of Prince Edward IslandUniversity of Prince Edward Island
Fundersnot available
KeywordsProcess (computing)Work (physics)Resource (disambiguation)Public relationsPolitical scienceHealthy eatingBusinessPsychologyMedicinePhysical activityEngineeringComputer science

Abstract

fetched live from OpenAlex

PURPOSE: The study was conducted to identify the enabling and barrier factors in the development of nutrition policies in Prince Edward Island elementary and consolidated schools. METHODS: A document review and in-depth interviews were conducted with key stakeholders (n=12). RESULTS: Principals were identified as important champions for change. Working group members created an interface between the school world and the nutrition world, and drew upon common philosophical ground to work together to lead the change process. Successfully navigating the process of policy development required building a case for change, testing policies in the real world, integrating healthy eating within school life, offering support to schools, engaging participants, and acknowledging the need to weigh the costs and benefits of the change. At times, external pressures on schools and available foods varied in the extent to which they enabled or challenged policy development. Finally, resource limitations, competing issues, and the use of unhealthy food as rewards were identified as the primary barriers. CONCLUSIONS: The use of a consultative approach that engages key stakeholders early in the process is critical to the successful development of school nutrition policies. This approach also may be an important predictor of the long-term success of such initiatives.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.387
Teacher spread0.313 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations35
Published2009
Admission routes2
Has abstractyes

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