Barriers and enablers to academic health leadership
Bibliographic record
Abstract
Purpose This study sought to identify the barriers and enablers to leadership enactment in academic health-care settings. Design/methodology/approach Semi-structured interviews ( n = 77) with programme stakeholders (medical school trainees, university leaders, clinical leaders, medical scientists and directors external to the medical school) were conducted, and the responses content-analysed. Findings Both contextual and individual factors were identified as playing a role in affecting academic health leadership enactment that has an impact on programme development, success and maintenance. Contextual factors included sufficient resources allocated to the programme, opportunities for learners to practise leadership skills, a competent team around the leader once that person is in place, clear expectations for the leader and a culture that fosters open communication. Contextual barriers included highly bureaucratic structures, fear-of-failure and non-trusting cultures and inappropriate performance systems. Programmes were advised to select participants based on self-awareness, strong communication skills and an innovative thinking style. Filling specific knowledge and skill gaps, particularly for those not trained in medical school, was viewed as essential. Ineffective decision-making styles and tendencies to get involved in day-to-day activities were barriers to the development of academic health leaders. Originality/value Programmes designed to develop academic health-care leaders will be most effective if they develop leadership at all levels; ensure that the organisation's culture, structure and processes reinforce positive leadership practices; and recognise the critical role of teams in supporting its leaders.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".