Understanding Unintentional Injury Risk in Young Children II. The Contribution of Caregiver Supervision, Child Attributes, and Parent Attributes
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it