648-P: Developing a National Competency-Based Diabetes Curriculum in Undergraduate Medical Education: A Delphi Study
Bibliographic record
Abstract
Introduction: In recent years there has been an increased emphasis on competency-based medical education (CBME) internationally, as can be seen with the implementation of competency-based curriculums for postgraduate medical education (PGME) through the Royal College of Physicians and Surgeons of Canada. Currently, no Canada-wide consensus exists on educational competencies relating to diabetes in undergraduate medical education (UGME). Objective: To develop a list of competencies and objectives for diabetes teaching in medical school using a modified Delphi method. Methods: Representatives involved in the development of the diabetes curriculum at all 17 medical schools across Canada were contacted. A draft list of competencies and objectives was developed by the research team using the existing curriculums at nine Canadian medical schools and was organized using the CanMEDS framework. A Delphi method was used, with two iterations in order to reach consensus. The first survey was conducted in May 2018. The data was analyzed and the survey was revised to include the opinions and remarks of respondents. The revised version of the survey was then sent to the respondents in July 2018. Results: Out of 17 medical schools contacted, 12 (70.6%) agreed to participate. One school declined participation and four did not respond. Out of 12 surveys sent in the first round, eight responses were received (response rate 66.7%). The revised version was then resent to the eight respondents in July 2018 and seven responses were received (response rate 87.5%). A list of nine competencies and 65 objectives was finalized. Conclusion: A competency-based consensus curriculum for diabetes education for medical students was developed using a modified Delphi method. The final consensus syllabus will be disseminated throughout the country. This curriculum serves as a step in the transition to CBME and in ensuring that future medical school graduates are proficient in diabetes care. Disclosure S. Shah: None. M. McCann: None. C. Yu: None.
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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 | 0.039 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".