Critical route for leadership competencies in medical students in 35 PAHO members states: A scoping review and thematic analysis
Bibliographic record
Abstract
The 35 member states (MS) of the Pan American Health Organization (PAHO) have been committed to training physicians in leadership competencies since 2008. However, four reviews on teaching leadership using competency-based education (CBE) in undergraduate medical education (UME) identified only two MS: Canada and the USA that worked on identifying gaps in teaching leadership in UME. Previous reviews did not focus on factors influencing leadership education and did not use qualitative methodology to support their findings. Therefore, this review aims to identify facilitating and inhibiting factors in teaching leadership in UME using a scoping review and thematic analysis. Six databases containing grey and indexed literature in English, Spanish, and Portuguese were searched including hand search and authors’ consultations. Forty-eight documents out of 7849 were selected based on eligibility criteria. Braun and Clarke’s thematic analysis guide was used, resulting in seven themes: curriculum, intended learning outcomes, teaching methods, assessment, addressing barriers, supporting organizational change, and building networks. Considering these themes, the authors propose a critical route for teaching leadership in UME in the Americas. First, institutional design should consider governance gaps, such as having national and international policies for leadership in UME with an inter-professional, trans-professional, and citizen-focused approach. This means that there is a pressing need to equip physicians and other professionals from the government, academia, non-governmental organizations, hospitals, and national and international organizations whose missions are related to health or education with leadership competencies. Networking among actors for leadership education and teacher training is also essential. Second, instructional design reveals knowledge-do gaps in MS when incorporating leadership into the medical curriculum. This includes using leadership frameworks, defining learning outcomes, and employing assessment and monitoring tools for leadership education. Mechanisms to reduce these gaps in MS include the Equator Network and Evidence-Informed Policy Networks which foster knowledge translation and governance.
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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 | 0.049 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.033 | 0.027 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".