Understanding Unintentional Injury Risk in Young Children II. The Contribution of Caregiver Supervision, Child Attributes, and Parent Attributes
Bibliographic record
Abstract
OBJECTIVE: To identify child and parent attributes that relate to caregiver supervision and examine how these factors influence child-injury risk. METHODS: Mothers completed diary records about supervision of their young child (2-5 years) when at home. Standardized questionnaires provided information about child attributes, maternal attributes, and children's history of injuries. RESULTS: Correlations revealed that child attributes and parent attributes related both to actual maternal supervision and child-injury scores. Regression analyses to predict injury scores revealed child-temperament factors alone predicted all levels of severity (minor, moderately severe, and medically attended), but parent supervision also contributed to predict medically attended injuries. CONCLUSIONS: Both child and parent factors influenced caregiver's supervision of young children at home and related to child-injury risk. For medically attended injuries, child attributes and parent supervision both predicted risk, whereas for less serious injuries, child factors alone determined risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".