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Record W7071411905

School-Level Perspectives of the Ontario School Nutrition Program

2022· article· en· W7071411905 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsFieldnotesProgram evaluationResearch programPublic healthWork (physics)School district
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the implementation of a school snack program for children in elementary schools. School-level program volunteers’ experiences were explored using semi-structured interviews. Fieldnotes were taken during on-site school visits. Quantitative data were collected through a General Information Form and Weekly Logbooks. Seven elementary schools in Southwestern Ontario were invited and agreed to participate. Interviews (n = 27) revealed that volunteers valued the program for its universality, the excitement it created, the opportunity for students to try new foods, and the social interactions that it generated. Challenges included the burden on snack volunteers to plan, procure, and prepare foods; the competition the program posed for school priorities; limited funding; and a lack of clear purpose for the program. Suggestions for improvement included providing adequate and sustained resources and an integration of the program into the curriculum. Data obtained from 15 on-site visits, 7 General Information Forms, and 59 (out of a total of 70) Weekly Logbooks confirmed the data obtained from interviews. This research provides insights into the challenges of volunteer-led school snack programs in Canada and may guide policy makers, practitioners, and researchers in the development of a universal, nationally funded school food program.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.047
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.048
GPT teacher head0.300
Teacher spread0.253 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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