Student-led leadership training for undergraduate healthcare students
Bibliographic record
Abstract
Purpose Effective clinical leadership is crucial to avoid failings in the delivery of safe health care, particularly during a period of increasing scrutiny and cost-constraints for the National Health Service (NHS). However, there is a paucity of leadership training for health-care students, the future leaders of the NHS, which is due in part to overfilled curricula. The purpose of this study was to assess the impact of student-led leadership training for the benefit of fellow students. Design/methodology/approach To address this training gap, a group of multiprofessional students organised a series of large-group seminars and small-group workshops given by notable health-care leaders at a London university over the course of two consecutive years. Findings The majority of students had not previously received any formal exposure to leadership training. Feedback post-events were almost universally positive, though students expressed a preference for experiential teaching of leadership. Working with university faculty, an inaugural essay prize was founded and student members were given the opportunity to complete internships in real-life quality improvement projects. Originality/value Student-led teaching interventions in leadership can help to fill an unmet teaching need and help to better equip the next generation of health-care workers for future roles as leaders within the NHS.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".