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Record W2179227102 · doi:10.1093/jpepsy/jsv070

Parent–Child Injury Prevention Conversations Following a Trip to the Emergency Department

2015· article· en· W2179227102 on OpenAlexaff
Elizabeth E. O’Neal, Jodie M. Plumert, Carole Peterson

Bibliographic record

VenueJournal of Pediatric Psychology · 2015
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsConversationInjury preventionSuicide preventionHuman factors and ergonomicsOccupational safety and healthPoison controlPsychologyLogistic regressionDevelopmental psychologyMedical emergencyMedicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The goal of the study was to examine how parents use conversation to promote the internalization of safety values after their child has been seriously injured. METHODS: Parent interviews detailing postinjury conversations were coded for strategies mentioned to prevent injuries in the future and information about circumstances surrounding the injury. RESULTS: Logistic regression analyses revealed that parents were more likely to discuss why an activity was dangerous with older than younger children, and were more likely to urge daughters than sons to be more careful in the future. Injuries resulting from the presence of environmental hazards predicted parents telling children to be more careful in the future. Having others involved predicted parents urging children not to engage in the behavior again. CONCLUSIONS: Findings suggest that parents modulated strategies according to age, gender, and injury circumstances to maximize the likelihood that children would behave differently in the future.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.067
GPT teacher head0.420
Teacher spread0.353 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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