Defining a “Healthy Role-Model” for Medical Schools: Learning Components That Count
Bibliographic record
Abstract
Michael Andreas Leman,1 Mora Claramita,2 Gandes Retno Rahayu2 1School of Dentistry, Faculty of Medicine, Universitas Sam Ratulangi, Manado, Indonesia; 2Department of Medical, Health Professions Education and Bioethics, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, IndonesiaCorrespondence: Michael Andreas LemanSchool of Dentistry, Faculty of Medicine, Universitas Sam Ratulangi, Jl. Kampus Unsrat, Bahu, Manado, IndonesiaTel +62 85340755557Email micpatlem1982@unsrat.ac.idIntroduction: Producing healthy physicians who act as a “healthy role-model” in their environment must be one of the concerns of medical schools today in response to the global movement of “health-promoting university” by the WHO (1995). However, no publications explained the “healthy role-model” in medical school. This study aimed to fill this gap by exploring the definition and characteristics of a “healthy role-model” for medical teachers.Methods: We used a grounded theory approach with in-depth interviews and e-mail communications to 48 medical teachers from various backgrounds of “health professions education,” “health education and behavior”/’health education and promoter,’ “general practitioners/family medicine,” “adolescent health,” “internal medicine,” and “cardiology-vascular medicine.” The medical teachers were from Indonesia, one other developing country (Bangladesh), and five developed countries (United States of America, Canada, Netherlands, Australia, and United Kingdom). We also invited 19 medical students from Indonesia for three focus group discussions.Results: We identified four categories to define a “healthy role-model” for medical schools as persons who are seen: 1) “physically,” “socially,” “mentally”, and “spiritually” healthy; 2) internalized healthy behaviors; 3) willing to promote healthy lifestyles; and, 4) a life-long learner. In each category, there are several characteristics discussed.Conclusion: Our study provides some insights to define a “healthy role-model” of medical teachers by using the characteristics of healthy people and adult learners. The first category describes the characteristics of healthy people, but cultural issues influence the perspectives of medical teachers to define a “healthy role-model” for medical schools.Keywords: healthy role-model, role-modeling, adult learner, grounded theory methodology
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".