Medical schools viewed from a political perspective: how political skills can improve education leadership
Bibliographic record
Abstract
OBJECTIVES: Political science offers a unique perspective from which to inform education leadership practice. This article views leadership in the health professions through the lens of political science research and offers suggestions for how theories derived from political science can be used to develop education leadership practice. POLITICAL SCIENCE RESEARCH: Political science is rarely used in the health professions education literature. This article illuminates how this discipline can generate a more nuanced understanding of leadership in health professions education by offering a terminology, a conceptual framework and insights derived from more than 80 years of empirical work. APPLICATION TO HEALTH PROFESSIONAL EDUCATION: Previous research supports the premise that successful leaders have a good understanding of political processes. Studies show current health professional education is characterised by the influence of interest groups. At the same time, the need for urgent reform of health professional education is evident. Terminology, concepts and analytical models from political science can be used to develop the political understanding of education leaders and to ultimately support the necessary changes. CONCLUSIONS: The analytical concepts of interest and power are applicable to current health professional education. The model presented - analysing the policy process - provides us with a tool to fine-tune our understanding of leadership challenges and hence to communicate, analyse and create strategies that allow health professional education to better meet tomorrow's challenges.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".