Pilot study for a preconception educational intervention for people with mobility disabilities
Bibliographic record
Abstract
OBJECTIVES: To assess the feasibility and acceptability of our newly designed, tailored preconception educational intervention for people with mobility disabilities. METHODS: A prospective pre-post pilot study to measure the feasibility and acceptability of an educational intervention for people with mobility disabilities who could become pregnant in the next five years. Before and after general health and preconception knowledge were compared using paired t-tests. RESULTS: 26 participants completed the study from November 2023 - July 2024. The most common diagnoses related to mobility disability were spinal cord injury (n = 9), spina bifida (n = 4), and cerebral palsy (n = 4). The intervention increased general health knowledge by 0.9 points (p = 0.13) and preconception health knowledge by 2.7 points (p = 0.025). A significant increase (1.8 points (95 % CI 0.9-2.6 points), p < 0.001) in preconception health knowledge compared to general health knowledge suggests the intervention successfully improved the targeted knowledge area. CONCLUSIONS: This pilot study demonstrated the feasibility and acceptability of a preconception educational intervention tailored to individuals with mobility disabilities. This intervention holds the potential to improve health literacy, increase preconception health knowledge, and ultimately improve pregnancy outcomes among people with mobility 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".