Contact with health services for adverse childhood experiences and subsequent risk of non-affective psychotic disorder: Population-based evidence from Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: We sought to estimate the association between indicators of health service contact for adverse childhood experiences (ACEs) and the risk of psychotic disorders using population-based health administrative data. METHODS: We accessed the Ontario-MINDS cohort, constructed using population-based health administrative data. The cohort included children born between 1992 and 1996, linked to maternal health records, and followed to age 27-31 years to identify incident non-affective psychotic disorder (NAPD) using a validated algorithm. We conducted a scoping review to identify codes indicative of ACE-related health service contacts prior to age 12 years, including indicators of abuse, neglect, and household dysfunction. Multivariable modified Poisson regression models were estimated to obtain incidence rate ratios (IRR) and 95 % confidence intervals (CI). RESULTS: In our analytic sample (n = 559,073), 27.1 % had a health service contact for one or more ACEs. The risk of NAPD was 51 % higher for those with a contact for household dysfunction (IRR = 1.51;95%CI = 1.45,1.58), 78 % higher for those with a contact for abuse/neglect (IRR = 1.78;95%CI = 1.58,2.01), and nearly three-fold higher among those who had health service contacts for both household dysfunction and abuse/neglect (IRR = 2.61;95%CI = 2.38,2.85). We also found a gradient effect, and people with health service contacts for 4+ ACE subtypes had a substantially elevated risk of NAPD (IRR = 4.04; 95%CI = 3.26,5.01), relative to those with no ACE-related contacts. CONCLUSIONS: Our findings add population-based evidence to the growing body of literature showing the detrimental effects of ACEs on serious mental disorders, and highlight the utility of administrative databases for advancing research in this field.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".