Smoking and Medication During Pregnancy Predict Repeated Unintentional Injuries in Early Childhood But Not Single Unintentional Injuries
Bibliographic record
Abstract
This study investigates prospectively the development of single and repeated unintentional injuries from birth to 42 months in a random population sample of new-born children in Quebec (Canada) (N = 1,770). The outcome measures are single unintentional injuries (SUI) and repeated unintentional injuries (RUI). Results showed that the risk factors for SUI differed from the risk factors for RUI. SUI was predicted by mother's antisocial behavior during high school (OR = 1.72) and mother's age at first birth (OR = 1.82) with children from older mothers at higher likelihood of SUI. Also, boys (OR = 1.36) and hyperactive children (OR = 1.06) were at increased risk of SUI. RUI was predicted by maternal smoking during pregnancy (OR = 1.68), medication on prescription (OR = 1.53) and medication without prescription (OR = 1.54). Boys (OR = 2.01), children with a difficult temperament (OR = 1.13) and those with single mothers had higher rates of RUI (OR = 2.05). Maternal perception of impact (OR = 1.15) and maternal feelings of self-efficacy (OR = 0.87; marginally significant) were also associated with RUI. These results show that maternal and child risk factors identified during pregnancy and just after birth can predict SUI as well as RUI in early childhood. However, the only common risk factor for SUI and RUI is the child's sex, with boys being at higher risk than girls. Implications of these findings and suggestions for prevention are discussed.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".