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Record W2034555132 · doi:10.1080/13575279.2010.541424

Factors Influencing Childcare Providers' Food and Mealtime Decisions: An Ecological Approach

2011· article· en· W2034555132 on OpenAlexaboutno aff
Meghan Lynch, Malek Batal

Bibliographic record

VenueChild Care in Practice · 2011
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyQualitative researchComprehensionVariety (cybernetics)Medical educationPublic relationsSociologyPolitical scienceMedicineSocial science

Abstract

fetched live from OpenAlex

To better understand and promote healthy nutritional behaviour development in children, research suggests the need to develop a stronger comprehension of influences from their social environment. Yet research has favoured studying parents, with little attention being paid to other important individuals in children's lives, especially from a qualitative research approach. Thus, the goal of this study was to understand the factors influencing childcare providers' decisions regarding nutrition in childcare settings. Semi-structured interviews were conducted with 13 home-based and centre-based childcare providers in the Ottawa region. Through use of the social ecological model, results revealed a comprehensive understanding of different personal, community, and societal factors that influence providers in their decisions regarding food and mealtimes. To promote healthy nutritional behaviours in children, the variety of factors that influence nutritional decisions by providers need to be addressed, given the amount of time Canadian children spend in early childcare settings.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.157
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.299
Teacher spread0.249 · 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 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
Published2011
Admission routes1
Has abstractyes

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