Development of Quality Indicators for Pregnancy Care of People With Disabilities Using a RAND-Modified Delphi Method
Bibliographic record
Abstract
OBJECTIVES: This study aimed to develop quality indicators (QIs) for pregnancy care of people with disabilities. METHODS: We used a RAND-modified Delphi method. We first conducted a scoping review of Medline, Embase, PsycInfo, and CINAHL (2004-2024) to identify candidate QIs related to the structures, clinical processes, and interpersonal processes of pregnancy care for people with disabilities. Draft QIs were then validated in a 3-round Delphi study from June 2023 to October 2024, with an expert panel of 17 pregnancy care providers and 10 birthing people with disabilities. In round 1, panellists rated draft QIs on importance and feasibility in a survey. New QIs and QIs requiring rephrasing were identified. In round 2, QIs were discussed and refined in focus groups. In round 3, panellists rated new and revised QIs on importance and feasibility. The final list of QIs was created on the basis of panel consensus on importance. RESULTS: The review identified 98 studies, from which 44 candidate QIs were created for structures (n = 12), clinical processes (n = 22), and interpersonal processes of care (n = 10). In round 1 of the Delphi survey, consensus on importance was achieved for all QIs, 5 of which were identified as requiring rephrasing. Panellists suggested 10 new QIs. In round 2, the new and revised QIs were discussed in focus groups. In round 3, the new and revised QIs achieved consensus on importance, resulting in a final list of 54 QIs (n = 43 achieving consensus on feasibility). CONCLUSIONS: These QIs can assist health care providers, administrators, and policymakers in optimising the quality of 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 | 0.248 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".