Inclusion of people with multiple long-term conditions in pregnancy research: patient, public and stakeholder involvement and engagement in a randomised controlled trial
Bibliographic record
Abstract
Abstract Background Both pregnant women and those with multiple long-term conditions are under-served groups in clinical research. Informing and improving research through patient and public involvement, including pregnant women with two or more long-term health conditions, is critical to increasing their inclusion in maternity research. Giant PANDA is a randomised controlled trial, evaluating the effect of a treatment initiation strategy with nifedipine versus labetalol on severe maternal hypertension and a composite outcome of fetal/neonatal death, or neonatal unit admission. We aimed to undertake a mixed methods study-within-a-project within the Giant PANDA trial to understand barriers and facilitators to participation, understand and optimise current representativeness of clinical trial delivery of those with multiple long-term conditions and co-create a checklist to support their inclusion in pregnancy research. Methods We undertook online workshops with women with lived experience and hybrid workshops with healthcare professionals who look after women with multiple long-term conditions. A site audit of Giant PANDA sites provided insights into research delivery capacity and health system set-up, and how this influences inclusion. An extension to the Giant PANDA screening log captured data on multiple long-term conditions enabling analysis of the impact of these health conditions on women’s inclusion in the trial. We co-created a checklist of recommendations for those designing and recruiting to similar clinical trials. Results Five key recommendations were identified including a need to (1) involve women with multiple long-term conditions as partners in maternity research and (2) minimise barriers that stop them from taking part through (3) designing and delivering research that is flexible in time and place (4) consider research as part of care for everyone, including those with multiple long-term conditions and (5) measure and report inclusion of those with two or more health conditions in maternity research. Multiple long-term conditions were not a barrier to recruitment or randomisation in the Giant PANDA trial. Conclusion Women with multiple long-term conditions would like opportunities to find out about and participate in research which accounts for their needs. Our checklist aims to support those designing and delivering maternity research to optimise inclusion of individuals with multiple-long term conditions. Trial registration: Giant PANDA: EudraCT number: 2020-003410-12, ISRCTN: 12,792,616.
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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 | 0.666 | 0.671 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".