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Record W2112088948 · doi:10.1093/jpepsy/jsj045

Understanding Unintentional Injury-Risk in Young Children I. The Nature and Scope of Caregiver Supervision of Children at Home

2005· article· en· W2112088948 on OpenAlexaff
Barbara A. Morrongiello, Michael Corbett, Meghan McCourt, N. G. Johnston

Bibliographic record

VenueJournal of Pediatric Psychology · 2005
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsScope (computer science)PsychologyInjury preventionDevelopmental psychologySuicide preventionHuman factors and ergonomicsOccupational safety and healthPoison controlMedicineMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the supervision that young children routinely receive when awake and at home with a parent. METHODS: Mothers were trained to complete continuous recordings about supervision of their young child (2-5 years) when at home on each of 10 randomly selected days within a 3-week period. RESULTS: Children were supervised more often than unsupervised but were completely out of view of supervisors about 20% of their awake time, and supervision was poorer when out of view of supervisors. Older children (4-5 years) were unsupervised (8% of awake time) more often than younger children (2-3 years; 1%), were more often out of view of supervisors than younger children, and received poorer supervision than younger children when out of view of supervisors. Few sex differences were found. CONCLUSIONS: These data provide insights into the nature and scope of supervision that young children routinely experience when at home. Implications of these findings for identifying patterns of supervision that elevate children's risk of injury are discussed.

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)0.006
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 score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.304
Teacher spread0.280 · 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

Citations98
Published2005
Admission routes1
Has abstractyes

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