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Record W3208764925 · doi:10.3390/ijerph182111108

Exploring Factors Contributing to the Implementation of Ontario’s Healthy Kids Community Challenge: Surveys and Key Stakeholder Interviews with Program Providers

2021· article· en· W3208764925 on OpenAlexaffabout
Michelle M. Vine, Rachel E. Laxer, Jessica Lee, Daniel W. Harrington, Heather Manson

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsPublic Health OntarioUniversity of TorontoBrock University
Fundersnot available
KeywordsThematic analysisStakeholderKey (lock)Public relationsProcess (computing)Health promotionMedical educationQualitative researchMedicineNursingPublic healthPolitical scienceSociologyComputer science

Abstract

fetched live from OpenAlex

(1) Background: To explore factors contributing to the Healthy Kids Community Challenge (HKCC) program implementation; (2) Methods: Data were collected through a quantitative survey (n = 124) and in-depth telephone interviews (n = 16) with program providers. Interviews were recorded and transcribed for thematic analysis using NVivo; (3) Results: Provincial funding and in-kind support from community partners were key. Initiatives were feasible to implement, and key messages were well-received by communities. Specific practices and process were commonly discussed, and strong local program leadership was crucial to implementation; (4) Conclusions: Results have implications for planning and implementing future multi-component, community-based health promotion programs that include similar partnerships.

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)0.014
metaresearch head score (Gemma)0.023
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 score0.970
Threshold uncertainty score0.649

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.023
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.842
GPT teacher head0.639
Teacher spread0.203 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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