The RESPCCT Study: Community-led Development of a Person-Centered Instrument to Measure Health Equity in Perinatal Services
Bibliographic record
Abstract
While Canadian maternal mortality rates suggest widespread access to high-quality care, perinatal health care outcomes and care experiences among pregnant people in Canada vary widely, particularly among communities that have been historically oppressed, excluded, and marginalized. The lack of patient-oriented research and measurement in perinatal services led to the RESPCCT (Research Examining the Stories of Pregnancy and Childbirth in Canada Today) Study which used a community participatory action research (CPAR) approach to examine experiences of pregnancy and childbirth care. In this paper, we describe co-creation of a person-centered survey instrument that measures respect, disrespect and mistreatment during pregnancy-related care of individuals with diverse identities, backgrounds and circumstances. The study was co-led by a Community Steering Council alongside a multi-disciplinary group of researchers and clinicians, and pilot tested by service users from across Canada. The final survey instrument includes items that assess respectful care across 17 domains, including validated measures of autonomy, respect, mistreatment, trauma, and discrimination. It also captures information about respondents’ identities, backgrounds, circumstances, access to care, provider type, and outcomes. A total of 6096 individuals participated in the survey. We describe how we implemented CPAR best practices, strengths, challenges, and lessons learned for instrument development in reproductive justice research.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.006 |
| 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".