Utility of self-reported mental health measures for preventing unintentional injury: results from a cross-sectional study among French schoolchildren
Bibliographic record
Abstract
BACKGROUND: Identify children at-risk of having mental health problems is of value to prevent injury. But the limited agreement between informants might jeopardize prevention initiatives. The aims of the present study were 1) to test the concordance between parents and children reports, and 2) to investigate their relationships with parental reports of children' unintentional injuries. METHODS: In a population-based sample of 1258 children aged 6 to 11, the associations between child psychopathology (using the Dominic Interactive and the Strengths and Difficulties Questionnaire) and unintentional injuries in the past 12 months were examined in univariate and multivariate models. RESULTS: As compared to children, parents tended to overestimate behavior problems and hyperactivity/inattention, and underestimate emotional symptoms. Unintentional injury in the last 12-month period was reported in 184 out of 1258 children (14.6%) and multivariate analyses showed that the risk of injury was twice as high in children self-reporting hyperactivity/inattention as compared to others. However this association was not retrieved with the parent-reported instrument. CONCLUSION: Our findings support evidence that child-reported measures of psychopathology might provide relevant information for screening and injury prevention purposes, even at a young age. It could be used routinely in combination with others validated tools.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".