Evaluation of a Question Prompt List About Cardiovascular Disease Risk and Prevention After Hypertensive Pregnancy: A Pilot Study
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to pilot test a question prompt list (QPL) about cardiovascular disease (CVD) risk reduction after hypertensive pregnancy (HDP). METHODS: In a prospective cohort study of adult women who had HDP given the QPL before and surveyed after a physician visit, we assessed perceived person-centred care, self-efficacy for self-management, perceived self-management and QPL feasibility. RESULTS: Twenty-three women participated: 57% of diverse ethno-cultural groups, 65% < 40 years of age and 48% immigrants. Most scored high for person-centred care (mean 4.1 ± 0.2/5); and moderately for self-efficacy (mean 7.4 ± 0.6/10) and self-management (mean 3.1 ± 0.3/5). Most appreciated QPL design and reported QPL benefits: helped them to prepare for the visit and know what to ask; increased confidence to ask questions, knowledge of the link between HDP and CVD and lifestyle behaviours to reduce CVD risk. Most reported that physicians were receptive to discussing QPL questions. CONCLUSION: Women appreciated the QPL and knowledge about self-management was high but self-efficacy for or perceived self-management was moderate. It appears feasible to share a QPL with ethno-culturally diverse women who can share it with physicians to facilitate discussions about post-pregnancy HDP-related CVD risk. PATIENT OR PUBLIC CONTRIBUTION: This study involved women who experienced HDP and engaged ethno-culturally diverse women with lived experience of HDP as study advisors in all stages of the 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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".