Preparing leaders in health professions education
Bibliographic record
Abstract
In the past 15 years, the number of Master's degree programs in Health Professions Education (MHPE) has grown from 7 to 121 programs worldwide. New MHPE programs continue to be developed each year, due to increased demand for individuals with specialized knowledge concerning how to best educate future health professionals. During the 2012 Association of Medical Education in Europe (AMEE) meeting in Lyon, France, a symposium was organized to explore the reasons for the proliferation of MHPE programs worldwide. In particular, the issues explored included the need for such programs, their outcomes in developing education leaders and scholars in HPE, and facilitators, barriers and models for initiating such programs. This paper synthesizes the discussion during this symposium. Some of the reasons for enrolling in a Master's degree program in HPE include the formal credential, knowledge of a number of theories and frameworks, new approaches to problems and ways of thinking, the mentored project, and networking and working with faculty and students. The uniqueness of being a trainee in an MHPE program is the immersion in the medical education environment and the assimilation of a new approach to scholarship and a new approach to leadership.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".