Leadership competencies for medical education and healthcare professions: population-based study
Bibliographic record
Abstract
OBJECTIVE: To identify and empirically investigate the dimensions of leadership in medical education and healthcare professions. DESIGN: A population-based design with a focus group and a survey were used to identify the perceived competencies for effective leadership in medical education. SETTING: The focus group, consisting of five experts from three countries (Austria n=1; Germany n=2; Switzerland n=2), was conducted (all masters of medical education), and the survey was sent to health professionals from medical schools and teaching hospitals in six countries (Austria, Canada, Germany, Switzerland, the UK and the USA). PARTICIPANTS: The participants were educators, physicians, nurses and other health professionals who held academic positions in medical education. A total of 229 completed the survey: 135 (59.0%) women (mean age=50.3 years) and 94 (41.0%) men (mean age=51.0 years). MEASURES: A 63-item survey measuring leadership competencies was developed and administered via electronic mail to participants. RESULTS: Exploratory principal component analyses yielded five factors accounting for 51.2% of the variance: (1) social responsibility, (2) innovation, (3) self-management, (4) task management and (5) justice orientation. There were significant differences between physicians and other health professionals on some factors (Wilk's λ=0.93, p<0.01). Social responsibility was rated higher by other health professionals (M=71.09) than by physicians (M=67.12), as was innovation (health professionals M=80.83; physicians M=76.20) and justice orientation (health professionals M=21.27; physicians M=20.46). CONCLUSIONS: The results of the principal component analyses support the theoretical meaningfulness of these factors, their coherence, internal consistency and parsimony in explaining the variance of the data. Although there are some between-group differences, the competencies appear to be stable and coherent.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".