Challenges, benefits, and strategies for delivering pregnancy care to people with disabilities: A qualitative study of service providers and decision-makers in Ontario, Canada
Bibliographic record
Abstract
ObjectivesTo (1) understand the challenges and benefits of providing pregnancy care to people with disabilities and (2) identify strategies to address challenges, from the perspectives of health care and social service providers and decision-makers.MethodsWe undertook a qualitative descriptive study in Ontario, Canada, of 31 health care and social service providers and decision-makers. Participants completed semi-structured interviews about their education, training, and clinical or administrative experience working with pregnant and/or parenting people with physical, sensory, and intellectual or developmental disabilities, including challenges and benefits in pregnancy care provision, programming, and policies, as well as their recommendations to improve care. We took a directed content analysis approach.ResultsParticipants identified challenges in providing pregnancy care to people with disabilities, including a lack of data to inform care, the influence of social determinants of health on disabled people's lives, inadequate infrastructure, poor coordination and communication across services, minimal disability-related training, and ableist attitudes among providers. Benefits to providing pregnancy care for people with disabilities included becoming advocates for system-level change, personal fulfillment, opportunities to confront one's own biases, and development of humility related to the expertise of people with disabilities. Reflecting on these challenges and benefits, participants identified strategies for improving care, through creative resource-sharing solutions, accessibility measures, interprofessional and coordinated care, enhanced provider training, and respectful care approaches.ConclusionsFindings show the need for changes at system, institutional, and service provider levels to improve pregnancy care for people with disabilities.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".