Who’s at the Table? A Scoping Review of Stakeholder Engagement in Medical Education Program Evaluation
Bibliographic record
Abstract
Program evaluation is essential for medical schools to demonstrate social accountability and identify areas for improvement in medical education (MEd). Although stakeholder engagement is crucial in program evaluation, no previous review has specifically examined the stakeholders involved in MEd program evaluation. This scoping review addresses this gap by identifying the stakeholders, their roles, and their levels of engagement in evaluating MEd programs, along with the facilitators and barriers to their participation. Through a systematic search across four databases, we identified 53 relevant studies out of 7206 screened. Our findings reveal seven primary stakeholder groups, with students and program directors being the most frequent participants. However, a significant gap exists in the representation of community members and patients, indicating a need for greater inclusion of these key stakeholders. Additionally, we found that stakeholders are primarily engaged as passive participants providing feedback rather than actively shaping the evaluation process. Facilitators and barriers to participation were identified from the participants' perspective, highlighting the need for further research to understand the viewpoints of active stakeholders, such as faculty and administrators. Future studies should also explore the impact of different evaluation approaches on stakeholder engagement to develop more inclusive and effective MEd program evaluations.
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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 | 0.090 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".