Learning while leading: a realist evaluation of an academic leadership programme
Bibliographic record
Abstract
INTRODUCTION: Many academic health centres and universities have implemented leadership development programmes; however, their potential impact in different contexts in healthcare remains unknown. We assessed the impact of an academic leadership development programme on the self-reported leadership activities of faculty leaders in their respective work contexts. METHODS: Ten faculty leaders who participated in a 10-month leadership development programme between 2017 and 2020 were interviewed. The realist evaluation approach was used to guide deductive content analysis, allowing concepts related to what works for whom, why and when to emerge from the data. RESULTS: Faculty leaders benefited in different ways depending on the organisational context (eg, culture) in which they reside and their individual contexts (eg, personal aspirations as a leader). Faculty leaders who have minimal mentorship in their leadership role gained an increased sense of community and belongingness with peer leaders and received validation in their personal leadership approach from the programme. Faculty leaders with accessible mentors were more likely than their peers to apply the knowledge they learnt to their work settings. Prolonged engagement among faculty leaders in the 10-month programme fostered continuity of learning and peer support that extended beyond programme completion. CONCLUSIONS: This academic leadership programme included participation of faculty leaders in different contexts, resulted in varying impacts on participants' learning outcomes, leader self-efficacy and application of acquired knowledge. Faculty administrators should look for programmes with a multitude of learning interfaces to extract knowledge, hone leadership skills and build networks.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".