Brief Report: Young Children's Risk of Unintentional Injury: A Comparison of Mothers' and Fathers' Supervision Beliefs and Reported Practices
Bibliographic record
Abstract
OBJECTIVE: There is increasing interest in understanding how parent supervision influences young children's risk of injury, but nearly all of this research has been conducted with mothers. The present study compared first-time mothers' and fathers' supervisory beliefs and reported practices, and related these scores to parental reports of their child's history of injuries. METHODS: Mothers and fathers of children 2-5 years each independently completed a telephone interview and previously validated questionnaires about their supervisory beliefs and practices and their child's history of injuries. RESULTS: Mothers and fathers provided similar reports of their child's injuries (minor, medically attended) and scored similarly on various supervision indices. Despite these similarities, the way mothers' and fathers' supervision indices related to children's injury history scores differed. Children's frequency of minor and medically attended injuries was predicted from maternal supervisory scores but not from paternal scores. CONCLUSIONS: Maternal supervision has more impact on children's risk of injury than paternal supervision, possibly because mothers spend more time with children than fathers.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".