A population-based study of the frequency and predictors of induced abortion among women with schizophrenia
Bibliographic record
Abstract
BACKGROUND: Induced abortion is an indicator of access to, and quality of reproductive healthcare, but rates are relatively unknown in women with schizophrenia. AIMS: We examined whether women with schizophrenia experience increased induced abortion compared with those without schizophrenia, and identified factors associated with induced abortion risk. METHOD: In a population-based, repeated cross-sectional study (2011-2013), we compared women with and without schizophrenia in Ontario, Canada on rates of induced abortions per 1000 women and per 1000 live births. We then followed a longitudinal cohort of women with schizophrenia aged 15-44 years (n = 11 149) from 2011, using modified Poisson regression to identify risk factors for induced abortion. RESULTS: Women with schizophrenia had higher abortion rates than those without schizophrenia in all years (15.5-17.5 v. 12.8-13.6 per 1000 women; largest rate ratio, 1.33; 95% CI 1.16-1.54). They also had higher abortion ratios (592-736 v. 321-341 per 1000 live births; largest rate ratio, 2.25; 95% CI 1.96-2.59). Younger age (<25 years; adjusted relative risk (aRR), 1.84; 95% CI 1.39-2.44), multiparity (aRR 2.17, 95% CI 1.66-2.83), comorbid non-psychotic mental illness (aRR 2.15, 95% CI 1.34-3.46) and substance misuse disorders (aRR 1.85, 95% CI 1.47-2.34) were associated with increased abortion risk. CONCLUSIONS: These results demonstrate vulnerability related to reproductive healthcare for women with schizophrenia. Evidence-based interventions to support optimal sexual health, particularly in young women, those with psychiatric and addiction comorbidity, and women who have already had a child, are warranted.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".