Patient Involvement in Medical Education Research: Results From an International Survey of Medical Education Researchers
Bibliographic record
Abstract
There are demands to involve patients in medical education research (MER). This study surveyed researchers to examine the extent and nature of patient involvement in MER. It obtained 283 completed surveys (response rate of 5%). Of the respondents, 153 (54.1%) indicated that they involve patients in MER. Of these respondents, 102 (66.7%) stated that patients are data sources in MER, 41 (26.8%) noted that patients are involved as advisors and/or reviewers, and/or 22 (14.4%) indicated that patients are involved as team members. These respondents reported that they involve patients to improve the relevance of their MER to patients (n = 99; 64.7%), connect MER to patient outcomes (n = 98; 64.1%), and improve the appropriateness of MER (n = 92; 60.1%). The 130 respondents who do not involve patients in MER do not involve them because they believe that their research topic(s) are irrelevant to patients (n = 68; 52.3%), they have limited resources for patient involvement (n = 40; 30.8%), and/or they do not know how to involve patients (n = 28; 21.5%). Researchers need to consider how they can conduct their MER with patients.
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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 | 0.033 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".