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Gaps in Childhood Injury Research and Prevention: What Can Developmental Scientists Contribute?

2008· article· en· W2093319753 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChild Development Perspectives · 2008
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsInjury preventionPsychologyDevelopmental ScienceHuman factors and ergonomicsPoison controlDevelopmental psychologySuicide preventionOccupational safety and healthLimitingPrevention scienceEarly childhoodAffect (linguistics)MedicinePsychiatryEnvironmental healthPsychological intervention

Abstract

fetched live from OpenAlex

Abstract Unintentional injury is the leading cause of pediatric mortality in most of the developed world. Contributions from epidemiology, pubic health, and engineering perspectives have yielded important insights into risk and protective factors, but recent calls for research stress the need for behavioral science to advance understanding and prevention of childhood injuries. Limiting its focus to children younger than 13 years, this article identifies 4 gaps in the literature on childhood injury and discusses how developmental science might address these research needs by (a) applying developmental theory and conceptual approaches to understand the processes by which children are injured, (b) examining the role of developmental processes in injury risk, (c) identifying the bases for group differences in injury related to gender and cultural influences, and (d) exploring how family processes and relationships affect injury risk.

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.

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)0.000
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 score0.155
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.041
GPT teacher head0.357
Teacher spread0.317 · 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