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Record W2027491952 · doi:10.3148/68.1.2007.7

<i>Menu Planning for Childcare Centres</i>: Practices and Needs

2007· article· en· W2027491952 on OpenAlexafffundvenue
Nadine Romaine, Linda Mann, Kim Kienapple, Bonnie Conrad

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2007
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMount Saint Vincent UniversityCapital District Health Authority
FundersMount Saint Vincent University
KeywordsQuality (philosophy)Sample (material)Stratified samplingDescriptive statisticsMedical educationStatistical analysisPsychologyMedicineStatistics

Abstract

fetched live from OpenAlex

PURPOSE: Childcare menu planners' relevant knowledge, attitudes, and practices were determined, as were the menu planning guidelines or tools needed and the nutritional adequacy and quality of menus in licensed full-day childcare centres in Nova Scotia. METHODS: An ethics committee-approved questionnaire was mailed to a stratified random sample of 101 licensed childcare centres across the province. Respondents were instructed to forward a copy of their current four-week menu for nutrient analysis and menu quality evaluation. RESULTS: Descriptive statistical data analysis from the returned questionnaires (n=35) indicated that fewer than 50% of the menu planners had relevant training and knowledge. Discrepancies exist between attitudes about good menu planning and practices. A positive finding was that most respondents used reliable resources for menu planning and expressed a desire for updated resources and ongoing training in child nutrition/ menu planning. A number of nutrient and menu quality deficiencies were identified from the menus submitted (n=28). A significant statistical correlation was found between menu planning training and higher menu quality scores. CONCLUSIONS: The results will be relevant to nutritionists in the development of effective resources and training for childcare centre menu planners.

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)0.004
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.010
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 head0.086
GPT teacher head0.421
Teacher spread0.335 · 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.

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

Citations29
Published2007
Admission routes3
Has abstractyes

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