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Record W4407303593 · doi:10.3138/cpp.2023-065

Why Are Young Children Not in Child Care? Typologies of Child Care Non-Use among Canadian Children under Six Years

2025· article· fr· W4407303593 on OpenAlexaffvenueabout
Karine J. Lavergne

Bibliographic record

VenueCanadian Public Policy · 2025
Typearticle
Languagefr
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsAboriginal Affairs Northern Dev Canada
Fundersnot available
KeywordsChild careMedicinePsychologyDevelopmental psychologyFamily medicinePediatrics

Abstract

fetched live from OpenAlex

L'article examine la non-utilisation de services de garde d'enfants avant les accords pancanadiens sur l'apprentissage et la garde de jeunes enfants intervenus entre le gouvernement fédéral et les gouvernements provinciaux et territoriaux et appuyés par un investissement fédéral de jusqu’à 30 milliards de dollars canadiens. À l'aide de données de l'Enquête sur les modes d'apprentissage et de garde des jeunes enfants de Statistique Canada recueillies en 2019 (avant la pandémie) et en 2020 (en cours de pandémie), l'auteure a mené des analyses de structure latente pour les raisons parentales de ne pas utiliser de services de garde et a cerné six typologies similaires de non-utilisation de services de garde pour les deux cohortes, plus une typologie de parents ayant à composer avec une pandémie en 2020. Certains parents ne semblaient pas désirer se prévaloir de services de garde (p. ex., parents choisissant de rester à la maison, parents accommodés par l’école, parents en congé du travail, parents au chômage). D'autres ont semblé ne pas avoir été en mesure de satisfaire leurs besoins en matière de services de garde, en raison d'obstacles liés aux frais des services ou de contraintes liées à la pandémie. L'auteure estime que la demande non satisfaite était équivalente à 201 858 enfants de moins de 6 ans (9 %) en 2019 et à 394 881 enfants (17 %) en 2020.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.038
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.007
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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

Labeled directly by 2 models reading the full record.

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
Published2025
Admission routes3
Has abstractyes

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