Leadership knowledge and behaviours: outcomes of a full-day leadership workshop focusing on personal growth in foundation doctors
Bibliographic record
Abstract
BACKGROUND: Effective clinical leadership is required at every level, including in Foundation doctors. Most leadership programmes neglect self-awareness and personal growth aspects of leadership training. We modified the Basildon Leadership Hub to focus on these aspects and evaluated the new programme. METHODS: Large group sessions were led by speakers with varied leadership roles, interspersed by breakout sessions incorporating experiential and reflective learning. Attendees answered anonymous surveys before, immediately after, and 2 months after the workshop, with 5-point Likert-scale responses (1=strongly disagree to 5=strongly agree) designed around reaction, knowledge and behaviour levels of evaluation. We assessed differences in median responses using the Mann-Whitney U test with Bonferroni-Holm correction. RESULTS: The full-day workshop was attended by 27 trainees, 93% of whom considered it enjoyable and relevant. Attendees agreed more strongly to the statements 'I am a leader' and 'I know how I can demonstrate and develop my own leadership knowledge, skills and behaviours' in postcourse versus precourse surveys (p<0.001). The follow-up survey had a poor response rate of 26% with non-significant differences. CONCLUSION: A full-day leadership workshop for Foundation doctors focusing on personal growth resulted in improvement in self-assessed precourse and postcourse knowledge and attitudes; however, poor follow-up response rate limited demonstration of sustained outcomes or changes in behaviour.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".