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Record W2098996538 · doi:10.1093/jpepsy/jsp011

Brief Report: Young Children's Risk of Unintentional Injury: A Comparison of Mothers' and Fathers' Supervision Beliefs and Reported Practices

2009· article· en· W2098996538 on OpenAlexaff
B. A. Morrongiello, Bryan Walpole, Brae Anne McArthur

Bibliographic record

VenueJournal of Pediatric Psychology · 2009
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsInjury preventionTelephone interviewHuman factors and ergonomicsOccupational safety and healthMinor (academic)Suicide preventionPoison controlParental supervisionMedicinePsychologyDevelopmental psychologyClinical psychologyPsychiatryMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: There is increasing interest in understanding how parent supervision influences young children's risk of injury, but nearly all of this research has been conducted with mothers. The present study compared first-time mothers' and fathers' supervisory beliefs and reported practices, and related these scores to parental reports of their child's history of injuries. METHODS: Mothers and fathers of children 2-5 years each independently completed a telephone interview and previously validated questionnaires about their supervisory beliefs and practices and their child's history of injuries. RESULTS: Mothers and fathers provided similar reports of their child's injuries (minor, medically attended) and scored similarly on various supervision indices. Despite these similarities, the way mothers' and fathers' supervision indices related to children's injury history scores differed. Children's frequency of minor and medically attended injuries was predicted from maternal supervisory scores but not from paternal scores. CONCLUSIONS: Maternal supervision has more impact on children's risk of injury than paternal supervision, possibly because mothers spend more time with children than fathers.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.006
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.032
GPT teacher head0.402
Teacher spread0.370 · 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

Citations54
Published2009
Admission routes1
Has abstractyes

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