Finding a path to growth as a leader: a medical learner perspective
Bibliographic record
Abstract
Physicians are often required to lead teams in clinical and non-clinical environments but may not receive formal training in advance of these opportunities. In this commentary, three medical learners discuss their views on leadership education in undergraduate and postgraduate medicine, arguing that leadership development should be more explicitly integrated into training programmes and that medical leaders need to be better recognised for their contributions to this field, much like expert clinicians, clinician-educators and clinician-scientists are recognised for theirs. After reviewing the published literature in this domain, reflecting on their experiences engaging with medical leaders and attending a leadership education summit, the authors conclude that, as initial steps towards improving leadership training in medical education, faculties and programmes should commit to incorporating leadership training into their curricula, and strive to deliberately connect learners interested in leadership with practising clinician-leaders with an eye towards improving learners’ leadership skills. These first steps could help to catalyse the necessary shift towards improved leadership education and better patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".