Preconception Health Characteristics of Women with Disabilities in Ontario: A Population-Based, Cross-Sectional Study
Bibliographic record
Abstract
Background: There is growing recognition that preconception health, defined as the health of all reproductive-age individuals, impacts reproductive and perinatal outcomes. Although women with disabilities are becoming pregnant at increasing rates, little is known about their preconception health. Our objective was to describe the preconception health characteristics of women with physical, sensory, and intellectual/developmental disabilities and compare these characteristics with women without disabilities. Materials and Methods: We conducted a population-based cross-sectional study of 15- to 44-year-old women with physical (n = 253,184), sensory (n = 93,170), intellectual/developmental (n = 8,986), and multiple disabilities (n = 29,868), and women without these disabilities (n = 2,307,822) using Ontario health administrative data (2017–2018). We described preconception health variables related to social determinants of health, physical health status, psychosocial well-being, history of assault, medication use, and continuity of primary care and compared women with and without disabilities in crude and age-standardized analyses, with standardized differences >0.10 indicating clinically meaningful results. Results: Women with physical, sensory, intellectual/developmental, and multiple disabilities had poorer preconception health than women without disabilities. Disparities were pronounced for physical health status, psychosocial well-being, use of potentially teratogenic medications, and history of assault. Of all groups, women with intellectual/developmental disabilities had the greatest disparities. Conclusion: Further research is needed to identify contributors to poor preconception health among women with disabilities and to develop tailored preconception health interventions to meet their unique needs and experiences.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".