Accidental injury, self‐injury, and assault among children of women with schizophrenia: a population‐based cohort study
Bibliographic record
Abstract
OBJECTIVE: We aimed to compare the risk for injury overall and by intent (accidental injury, self-injury, and assault) among children born to women with versus without schizophrenia. METHODS: Using health administrative data from Ontario, Canada, children born from 2003 to 2017 to mothers with (n = 3769) and without (n = 1,830,054) schizophrenia diagnosed prior to their birth were compared on their risk for child injury, captured via emergency department, hospitalization, and vital statistics databases up to age 15 years. Cox proportional hazard models generated hazard ratios for time to first injury event (overall and by intent), adjusted for potential confounders (aHR). We stratified by child sex and age at follow-up: 0-1 (infancy), 2-5 (pre-school), 6-9 (primary school), and 10-15 (early adolescence) planning to collapse age categories as needed to obtain stable and reportable estimates. RESULTS: Maternal schizophrenia was associated with elevated risk for child injury overall (105.4 vs. 89.4/1000 person-years (py), aHR 1.08, 95% CI 1.03-1.14), accidental injury (104.7 vs. 88.1/1000py, 1.08, 1.03-1.14), for self-injury (0.4 vs. 0.2/1000py, 2.14 1.18-3.85), and assault (1.0 vs. 0.3/1000py, 2.29, 1.45-3.62). By child sex, point estimates were of similar magnitude and direction, though not all remained statistically significant. For accidental injury and self-injury, the risk associated with maternal schizophrenia was most elevated in 10-15-year-olds. For assault, the risk associated with maternal schizophrenia was most elevated among children in the 0-1 and 2-5-year-old age groups. CONCLUSION: The elevated risk of child injury associated with maternal schizophrenia, especially for self-injury and assault, suggests that targeted monitoring and preventive interventions are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".