Health characteristics of reproductive-aged autistic women in Ontario: A population-based, cross-sectional study
Bibliographic record
Abstract
While an increasing number of girls and women are being identified with autism, we know little about the health of reproductive-aged autistic women. Our objectives were to (1) describe health characteristics of reproductive-aged autistic women who could potentially become pregnant and (2) compare these characteristics with those of non-autistic women. We conducted a population-based cross-sectional study using 2017–2018 administrative health data from Ontario, Canada. A total of 6,870 fifteen- to 44-year-old autistic women were identified and compared with 2,686,160 non-autistic women. Variables of interest included social determinants of health (neighborhood income, residential instability, material deprivation, rurality), health (co-occurring medical and psychiatric conditions, use of potentially teratogenic medications, history of assault), and health care factors (continuity of primary care). Overall, reproductive-aged autistic women had poorer health compared with non-autistic women, including increased rates of material deprivation, chronic medical conditions, psychiatric conditions, use of potentially teratogenic medications, and history of assault. These findings highlight the need for health interventions tailored to the needs of reproductive-aged autistic women. Lay abstract While an increasing number of girls and women are being identified with autism, we know little about reproductive-aged autistic women’s health. This study used administrative data from Ontario, Canada, to compare the health of reproductive-aged autistic women with non-autistic women. Overall, reproductive-aged autistic women had poorer health compared with non-autistic women, including increased rates of material deprivation, chronic medical conditions, psychiatric conditions, history of assault, and use of potentially teratogenic medications (i.e. drugs that can be harmful to the development of an embryo or fetus). These findings suggest that there is a need for health interventions tailored to the needs of reproductive-aged autistic women.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".