Implementation of a Pilot Leadership Curriculum for Physical Medicine and Rehabilitation Residents
Bibliographic record
Abstract
ABSTRACT: Medical trainees are expected to achieve leadership competencies by the end of their training. However, there is a lack of standardized postgraduate leadership education. The aims of this study were to evaluate a pilot program consistent with leadership aims of the medical education body and to assess learners' perceived responses to the curriculum. A pilot workshop was developed using Kern's six-step approach to curriculum development for medical education. Topics included leading teams, managing conflict, feedback, goal setting, and time management, as these gaps were identified during a targeted needs assessment. Learning was assessed by preworkshop and postworkshop self-assessments, and the curriculum was evaluated with a postworkshop survey. The workshop was attended by 14 physical medicine and rehabilitation residents and 1 medical student. There was a statistically significant increase in participants' Likert scale confidence scores for the summative areas of leading teams, managing conflict, feedback, goal setting, and time management (P < 0.001). All participants rated the session as 4 or 5/5 on all evaluation domains. In conclusion, a single session targeting stated needs of trainees was successful in increasing perceived competence in areas relevant to clinical leadership. Expansion to include a longitudinal component, with assessment for behavior change for ongoing improvement would be beneficial.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".