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Record W2796425527 · doi:10.1111/cdep.12287

Preventing Unintentional Injuries to Young Children in the Home: Understanding and Influencing Parents’ Safety Practices

2018· article· en· W2796425527 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 · 2018
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIntervention (counseling)Occupational safety and healthInjury preventionSuicide preventionChild safetyHuman factors and ergonomicsBest practicePsychologyPoison controlMedicineNursingMedical emergencyEngineeringPolitical science

Abstract

fetched live from OpenAlex

Abstract Unintentional injuries are the leading cause of preventable deaths for children in most industrialized countries. In this article, I consider research on how parents prevent home injuries to children under 6 years and discuss an intervention aimed at improving parents’ home-safety practices. Parents of young children use three types of home-safety practices: teaching about safety, modifying the environment, and supervising. Relying predominantly on teaching increases young children's risk of injury, whereas modifying the environment and supervising protect children and predict fewer injuries. Drawing on evidence about factors that motivate parents’ safety practices, an intervention was developed to improve supervision: The Supervising for Home Safety program positively changed parents’ appraisals about injury and supervision practices. Developing evidence-based injury-prevention programs is an effective way to address this national public-health issue.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.498

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 studies0.0010.000
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 head0.037
GPT teacher head0.335
Teacher spread0.298 · 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