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Record W4400149960 · doi:10.1016/j.cdnut.2024.102415

Barriers and Facilitators to Implementing School Food Programs: Perspectives From Private Sector Partners

2024· article· en· W4400149960 on OpenAlexaff
Samantha Cheng, Mavra Ahmed, Tammy Lai, Selina Mae Quibrantar, Daniel Sellen

Bibliographic record

VenueCurrent Developments in Nutrition · 2024
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsPrivate sectorBusinessFood servicePublic relationsMedical educationPolitical scienceEconomic growthMarketingMedicineEconomics

Abstract

fetched live from OpenAlex

Objectives: Canada is the only OECD country that does not have a national school food program (SFP). There is a renewed focus on establishing a national SFP in Canada incorporating perspectives from key stakeholders to inform its development. There is currently no study examining the perspectives from private sector organization members (i.e., those who facilitate school food programs) in Canada. This study aims to gain insight on the facilitators and barriers of implementing SFPs from private sector partners.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.028
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.004
Scholarly communication0.0080.004
Open science0.0010.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.159
GPT teacher head0.477
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2024
Admission routes1
Has abstractno

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