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Record W2129802149 · doi:10.24095/hpcdp.33.1.01

Child care: implications for overweight / obesity in Canadian children?

2012· article· en· W2129802149 on OpenAlexaffvenueabout
Lindsay McLaren, Maiah Zarrabi, DJ Dutton, MC Auld, JCH Emery

Bibliographic record

VenueChronic diseases and injuries in Canada · 2012
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of VictoriaUniversity of Calgary
Fundersnot available
KeywordsOverweightPercentileMedicineObesityChildhood obesityBody mass indexDemographyHealth careGerontologyPolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: Over recent decades, two prominent trends have been observed in Canada and elsewhere: increasing prevalence of childhood overweight and obesity, and increasing participation of women (including mothers) in the paid labour force and resulting demand for child care options. While an association between child care and children's body mass index (BMI) is plausible and would have policy relevance, its existence and nature in Canada is not known. METHODS: Using data from the National Longitudinal Survey of Children and Youth, we examined exposure to three types of care at age 2/3 years (care by non-relative, care by relative, care in a daycare centre) in relation to change in BMI percentile (continuous and categorical) between age 2/3 years and age 6/7 years, adjusting for health and sociodemographic correlates. RESULTS: Care by a non-relative was associated with an increase in BMI percentile between age 2/3 years and age 6/7 years for boys, and for girls from households of low income adequacy. CONCLUSION: Considering the potential benefits of high-quality formal child care for an array of health and social outcomes and the potentially adverse effects of certain informal care options demonstrated in this study and others, our findings support calls for ongoing research on the implications of diverse child care experiences for an array of outcomes including those related to weight.

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 score0.011
Threshold uncertainty score0.933

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 science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.243
Teacher spread0.238 · 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

Citations25
Published2012
Admission routes3
Has abstractyes

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