Disability justice and collective access to labour and delivery care: a qualitative study
Bibliographic record
Abstract
BACKGROUND: People with disabilities experience perinatal health disparities. This qualitative study examines disabled people's experiences of labour and delivery care from a disability justice lens. METHODS: Semi-structured interviews were conducted between July 2019 and February 2020 with 31 women and transgender people aged 18-45 years with physical, sensory, and/or intellectual/developmental disabilities, who were living in in Ontario, Canada and had given birth in the previous five years. RESULTS: People with disabilities described negative experiences of provider-driven, disrespectful, and discriminatory labour and delivery care that can be interpreted as examples of disability injustice and obstetric ableism. People with disabilities also described positive experiences of collaborative, respectful, and disability-affirming labour and delivery care that can be interpreted as examples of disability justice, facilitated by what feminist disability justice scholars and activists call collective access. CONCLUSIONS: Collective access to labour and delivery care can improve perinatal health care for people with disabilities and promote disability justice. Reimagining care-related decision-making as an interdependent, collaborative, respectful, and disability-affirming process shared between patients and providers can help to facilitate collective access to labour and delivery care.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".