Prenatal Exposure to Intimate Partner Violence and Developmental Health in Children at Kindergarten: Linking Canadian Population-Level Administrative Data.
Bibliographic record
Abstract
ObjectiveUsing population-wide administrative data, the objective was to provide Canadian evidence on the longitudinal relationship between maternal intimate partner violence (IPV) victimization and children’s developmental health. Using provincial prosecution records, we examined developmental vulnerability (DV) at kindergarten of children prenatally exposed to maternal IPV victimization compared to unexposed counterparts. ApproachThis retrospective cohort study linked administrative datasets (legal, health, education, social services) from the Population Research Data Repository at the Manitoba Centre for Health Policy. Exposed mother-child pairs with 1+ prosecution record of maternal IPV victimization during pregnancy between 2003 and 2018 in Manitoba (n = 1,117) were matched to unexposed pairs (1:3) based on sex/birthdate of child and neighbourhood income. DV at kindergarten was measured across 5 domains (physical, social, emotional, language/cognitive [LC], communication/general knowledge) using the Early Developmental Instrument (EDI). Children without eligible EDI scores were excluded. Multiple logistic regression models were conducted to address the objective. ResultsThe eligible cohort included 927 children (exposed n=229, unexposed n=698); 31.07% of the cohort was developmentally vulnerable in one or more domains (1/+) and 19.53% was developmentally vulnerable in two or more domains (2/+). Children who were prenatally exposed to maternal IPV victimization had increased odds of vulnerability across all 5 developmental domains (e.g., physical health/wellness: OR=2.83[1.95,4.10]; LC development: OR=2.45[1.65,3.64]). Unadjusted ORs showed statistically significant associations between maternal exposure of prenatal IPV victimization and DV in 1/+ (OR=2.70[1.98,3.68]) and 2/+ (OR=2.48[1.75,3.50]). When adjusted for covariates (e.g., maternal income assistance, mental health, child abuse history), no statistically significant relationship was found for any of the domains (e.g., LC development: aOR=0.98[0.53,1.81]), 1/+ (aOR=1.17[0.72,1.88]), and 2/+ (aOR=1.14[0.67,1.95]). ConclusionThe unadjusted, statistically significant associations suggest children exposed to maternal IPV victimization prenatally may face associated social/health risks. The finding highlights the need to consider potential factors that put children at risk of DV when developing and implementing support systems/interventions for children exposed to maternal IPV victimization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".