“Glocalization” in medical education: A framework underlying implementing CBME in a local context
Bibliographic record
Abstract
BACKGROUND/PURPOSE: The implementation of competency-based medical education is a social construction process within a local and cultural context. However, little is known about the process of adaptation to different systems, known as "glocalization". We analyzed the documents in the development of a milestone project from adapting global standards into a local context and identified a framework underlying this process. METHODS: Taiwan Society of Emergency Medicine (TSEM) had developed learning milestones based on the ACGME's version through series of consensus methods including committee work, nominal group technique (NGT), and a modified Delphi method. We applied qualitative content analysis to characterize the evolution of the three versions of TSEM and the original ACGME milestones documents and to explore the meaning behind the differences revealed by the glocalization process. RESULTS: We found 48 differences between ACGME and TSEM milestones. Among these differences, one was made by committee work, 44 came from NGT, and 3 were from the modified Delphi process. Two themes and seven sub-themes emerged from the coding process to explain the contextualization process of the milestones. CONCLUSION: We identified a framework that incorporates local expression and local needs into the process called glocalization through which global models of competency-based standards could be optimally implemented in a local context with different systems and cultures.
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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 | 0.071 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.014 | 0.072 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.006 | 0.007 |
| 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".