Patient and public involvement in pragmatic trials: online survey of corresponding authors of published trials
Bibliographic record
Abstract
BACKGROUND: There are few data on patient and public involvement (PPI) in pragmatic trials. We aimed to describe the prevalence and nature of PPI within pragmatic trials, describe variation in prevalence of PPI by trial characteristics and compare prevalence of PPI reported by trial authors to that reported in trial publications. METHODS: We applied a search filter to identify pragmatic trials published from 2014 to 2019 in MEDLINE. We invited the corresponding authors of pragmatic trials to participate in an online survey about their specific trial. RESULTS: Of 3163 authors invited, 2585 invitations were delivered, 710 (27.5%) reported on 710 unique trials and completed the survey; 334 (47.0%) conducted PPI. Among those who conducted PPI, for many the aim was to increase the research relevance (86.3%) or quality (76.5%). Most PPI partners were engaged at protocol development stages (79.1%) and contributed to the co-design of interventions (70.9%) or recruitment or retention strategies (60.5%). Patient and public involvement was more common among trials involving children, trials conducted in the United Kingdom, cluster randomized trials, those explicitly labelled as "pragmatic" in the study manuscript, and more recent trials. Less than one-quarter of trials (22.8%) that reported PPI in the survey also reported PPI in the trial manuscript. INTERPRETATION: Nearly half of trialists in this survey reported conducting PPI and listed several benefits of doing so, but researchers who did not conduct PPI often cited a lack of requirement for it. Patient and public involvement appears to be significantly underreported in trial publications. Consistent and standardized reporting is needed to promote transparency about PPI methods, outcomes, challenges and benefits.
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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 | 0.218 | 0.616 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".