Supervision and risk of unintentional injury in young children
Bibliographic record
Abstract
OBJECTIVE: Assess the association between caregiver supervision and acute unintentional injury in young children; evaluate whether lower levels of supervision result in more severe injury. METHODS: A case cross-over study was conducted. Parents of children aged ≤4 years whose injuries required emergency department (ED sample) treatment or admission to the hospital (inpatient sample) were interviewed. Information on supervision (3 dimensions: proximity, attention, continuity) at the time of injury and 1 h before the injury (control time) was collected. An overall supervision score was created; a higher score indicates closer supervision. Hospital admission served as a proxy for injury severity. ORs and 95% CIs were calculated. RESULTS: Interviews were completed by 222 participants; 50 (23%) were in the inpatient sample. For each supervision dimension the inpatient sample had higher odds of injury, indicating effect modification requiring separate analyses for inpatient and ED samples. For both samples, proximity 'beyond reach' was associated with the highest odds of injury; compared with 1 h before injury, children were more likely to be beyond reach of their caregiver at the time of injury (inpatient sample: OR 11.5, 95% CI 2.7 to 48.8; ED sample: OR 2.9, 95% CI 1.8 to 4.9). Children with lower supervision scores had the greatest odds of injury (inpatient sample: OR 8.0, 95% CI 2.4 to 26.6; ED sample: OR 3.3, 95% CI 1.9 to 5.6). CONCLUSIONS: Lower levels of adult supervision are associated with higher odds of more severe injury in young children. Proximity is the most important supervision dimension for reducing injury 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".