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Record W4416905166 · doi:10.1177/13591053251392034

Father-child and mother-child activation relationships as predictors of injury-risk behaviors in preschool boys and girls

2025· article· en· W4416905166 on OpenAlexafffund
Daniel Paquette, Julio Macario de Medeiros, Sophie Couture, Marc Bigras, Jean‐Pascal Lemelin

Bibliographic record

VenueJournal of Health Psychology · 2025
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversité du Québec à MontréalUniversité de SherbrookeUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLongitudinal studyPhysical activityInjury preventionHuman factors and ergonomicsPoison controlSuicide preventionInterpersonal relationshipEl Niño

Abstract

fetched live from OpenAlex

Boys are known to take more risks than girls, which may lead them to suffer more injuries than girls when exploring their physical surroundings. The aims of this study are to verify whether the father-child activation relationship has a greater effect than the mother-child activation relationship on preschoolers' risk-taking, and whether both of these relationships are a greater predictor of risk-taking in boys than in girls. Activation relationships were assessed using a standardized observation procedure at 15 and 45 months old with each parent. Both parents completed a questionnaire on their child's injury-risk behaviors during the preschool years. Both predictions were supported. Moreover, this longitudinal study found that preschoolers' risk-taking was predicted by mother-child overactivation in both infancy and preschool, whereas preschoolers' risk-taking was predicted by father-child overactivation in preschool alone, after controlling for parent-child overactivation relationships during infancy.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.024
GPT teacher head0.403
Teacher spread0.379 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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