Development and preliminary validation of the Coronary Artery Disease Education Questionnaire for Postpartum Women with pregnancy-related cardiometabolic complications (CADE-Q-PP): a modified Delphi approach
Bibliographic record
Abstract
AIMS: Pregnancy-related cardiometabolic complications such as gestational diabetes mellitus, hypertensive disorders, and preterm birth significantly increase the risk of future cardiovascular diseases (CVD). Many postpartum women remain unaware of this risk, highlighting the need for targeted educational interventions. Validated tools to assess knowledge gaps in this population are lacking. Therefore, this study aimed to develop and preliminarily validate the Coronary Artery Disease Education Questionnaire for Postpartum Women (CADE-Q-PP) to ensure content validity and relevance. METHODS AND RESULTS: The CADE-Q was systematically revised to identify items specific to postpartum women's knowledge concerning pregnancy-related cardiometabolic complications and CVD risk. A modified Delphi process was conducted with a panel of 28 international experts to refine the items, using a five-point Likert scale for consensus (mean score ≥4). Items were further simplified into plain language and a clarity assessment was completed with 20 postpartum women. A total of 61 items were drafted across four key knowledge areas: cardiovascular risk, physical activity, mental health, and nutrition. Through iterative discussion, consensus was achieved on 22 questionnaire items. Clarity assessment revealed a high degree of understanding among postpartum women (total mean 4.3 ± 0.9), with 20/22 items scoring above 4.0. Items that scored lower concerned long-term risks and specific interventions. Suggestions included sentence structure and providing context for terms like 'hidden sugar' and 'extra vitamins'. CONCLUSION: The CADE-Q-PP was developed as an accessible tool for clinicians to assess knowledge gaps regarding cardiovascular risk and health promotion in postpartum women. Future work will include testing of psychometric properties to confirm validity.
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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 | 0.047 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".