Consensus Preconception Educational Domains for People With Mobility Disabilities: A Delphi Study
Bibliographic record
Abstract
BACKGROUND: Preconception health education is critical to improve pregnancy and neonatal outcomes, but people with mobility disabilities have specific, often unique issues related to preparing for pregnancy. This study sought to develop consensus-based domains for a preconception education curriculum for people with mobility disabilities. METHODS: We used a mixed-methods approach, including a literature review and a Delphi method to develop consensus. Delphi panel members and domains were identified by a systematic review and purposive sampling and the panel included physicians, researchers, and individuals with lived experience of mobility disabilities. A Delphi method was used to reach consensus on domains for a preconception education curriculum for people with mobility disabilities. RESULTS: The systematic review identified 53 domains of preconception health education. Seventeen individuals participated in the Delphi panel process. After three rounds of quantitative and qualitative consensus feedback, 13 educational domains were selected for preconception education for people with mobility disabilities. The domains were 1) Pregnancy Interest; 2) Infertility and Obstetric History; 3) Genetic History and Screening; 4) Medical History; 5) Medication History; 6) Mental Health History; 7) Nutrition and Weight History; 8) Social Determinants of Health; 9) Intimate Partner Violence and Caregiver Abuse; 10) Functional Mobility and Physical Accommodations; 11) Musculoskeletal and Skin Health; 12) Bowel and Bladder Surgery; and 13) Neurological and Neurosurgical History. CONCLUSIONS: A consensus preconception education curriculum for people with mobility disabilities includes standard domains plus additional domains focused on functional mobility and physical accommodations: musculoskeletal and skin health, bowel and bladder surgery, and neurological and neurosurgical history.
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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 | 0.089 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".