Identifying leadership in medical trainees: evaluation of a competency-based approach
Bibliographic record
Abstract
BACKGROUND: As medical professional roles diversify, it is essential to understand what makes effective medical leaders. This study develops and validates a medical leadership competency framework that can be used to develop and evaluate leaders across all levels of medical organisations. METHOD: In Phase One, the authors derived desired leadership traits and behaviours in the medical context from a panel of subject matter experts (SMEs). Traits and behaviours were then combined into multifaceted competencies which were ranked and further refined through evaluation with additional SMEs. In Phase Two, the final seven competencies were evaluated with 181 medical trainees and 167 supervisors between 2017 and 2018 to determine the validity of rapid-form and long-form leadership assessments of medical trainees. Self and supervisor reports of the seven competencies were compared with validated trait and leadership behaviour measures as well as clinical performance evaluations. RESULTS: The final seven leadership competencies were: Ethical and Social Responsibility, Civility, Self-Leadership, Team Management, Vision and Strategy, Creativity and Innovation, and Communication and Interpersonal Influence. Results demonstrate initial validity for rapid-form and long-form leadership evaluations; however, perceptions of good leadership may differ between trainees and supervisors. Further, negative leadership behaviours (eg, incivility) are generally not punished by supervisors and some positive leadership behaviours (eg, ethical leadership) were associated with poor leadership and clinical performance evaluations by supervisors. Supervisor perceptions of leadership were significantly driven by trainee scores on social boldness (a facet of extraversion). CONCLUSIONS: A multicompetency framework effectively evaluates leadership in medicine. To more effectively reinforcepositive leadership behaviours and discourage negative leadership behaviours in medical students and resident physicians, we recommend that medical educators:: (1) Use validated frameworks to build leadership curriculum and evaluations. (2) Use short-term and long-term assessment tools. (3) Teach assessors how to evaluate leaders and encourage positive leadership behaviours early in training.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".