Identifying predictors of medically-attended injuries to young children: do child or parent behavioural attributes matter?
Bibliographic record
Abstract
OBJECTIVE: To investigate whether one can differentiate injured and uninjured young children based on child behavioural attributes or indices of caregiver supervision. METHOD: A matched case-control design was used in which case participants were children presenting to an emergency department for treatment for an injury and age/sex matched control participants presented for illness-related reasons. During structured phone interviews about supervision parents reported on general supervisory practices (standardised questionnaire) and specific practices corresponding to time of injury (cases) or the last time their child engaged in the activity that incited their match's injury (controls). Parents also reported on child behavioural attributes that have been linked to child risk taking in prior research (inhibitory control, sensation seeking). RESULTS: Results revealed no group differences in child behavioural attributes; however, the control group received more supervision both in general (OR = 4.82, 95% CI 1.89 to 12.33) and during the specified activity that led to injury in cases (OR = 5.38, 95% CI 2.13 to 13.58). CONCLUSION: These findings confirm past speculation that caregiver supervision influences children's risk of medically-attended injury and highlight the importance of targeting supervision in child-injury prevention interventions.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".