Global research agenda for medical education regulation: findings from a nominal group consensus exercise
Bibliographic record
Abstract
BACKGROUND: Although medical education regulation is widely practised and given substantial resource and priority by policymakers and leaders, there is little empirical evidence to support it or guide regulation practices at an international level. In recent years, international and cross-border accreditation systems have gained prominence, often linked to migratory opportunities for graduating physicians. Given the high-stakes nature of regulation in medical education, there is a pressing need for research in this area, including the development of a framework to guide how to prioritise the different areas of scholarly inquiry that need to be addressed to best inform and elevate accreditation practices. METHODS: This article reports a nominal group technique consensus exercise on global medical education regulation conducted in August 2023 in London, UK. Participants were invited based on their research and leadership roles in medical education regulation around the world. Working in three groups using the nominal group technique, participants examined issues associated with medical education regulation globally that required research and evaluation. RESULTS: 18 participants from 11 countries took part. There was remarkable consistency across the three groups. Each group identified over 15 areas of inquiry summarised in seven overall research domains: Purpose, Quality and Sustainability, Economics, Governance, Colonialism, Process and Outcomes. DISCUSSION: Regulation is ubiquitous in medical education, and a panel of international scholars and leaders identified a pressing set of global issues that require exploration to inform future practices. This research agenda can help policymakers and researchers understand and embrace the complexity that underlies this topic and use it to prioritise research efforts in the years ahead.
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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 | 0.265 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".