40 The qualities of a good leader: views of junior doctors
Bibliographic record
Abstract
Introduction There is a growing emphasis on developing effective leadership within healthcare, including better training and support for clinical leaders.1 The views and experiences of senior NHS leaders have recently been explored2 3; however, junior doctors remain an untapped resource. In order to develop our trainees into future clinical leaders, it is important to explore their current understanding of leadership. Methods We surveyed 97 junior doctors at a teaching hospital in Devon. Respondents identified skills and qualities they associated with good leadership and also described how they exhibit leadership during daily practice. The free-text responses received were then appraised within contemporary leadership theory. Respondents were mainly aged 21–30, FY1 - CT2 trainees and from General Practice, Medical Training (Core & Specialty) and Obstetrics and Gynaecology programmes. Results Juniors’ definitions of effective clinical leadership valued communication and teamwork skills over more technical proficiencies. Approachability and inclusivity were also highly valued, along with attributes associated with compassion, the development of others, consultative leadership and active followership. The junior doctors surveyed described their personal clinical leadership as a network of relationships and connections rather than single projects or set hierarchical roles. However, many failed to identify any examples of personal leadership, citing a lack of experience and isolated working in community settings. Conclusion This survey has explored junior doctors’ perceptions and experiences of leadership. Trainees valued collaboration skills and characteristics associated with the creation of a strong social identity, active followership and distributive leadership.4 These align with a transformational leadership style, compared with the more traditional, transactional leadership approach favoured during a study of their peers over a decade ago.5 References Warren OJ, Carnall R. Medical leadership: why it’s important, what is required and how we develop it. Postgrad Med J 2011;87:27–32. https://pmj.bmj.com/content/postgradmedj/87/1023/27.full.pdf Nicol ED, Mohanna K, Cowpe J. Perspectives on clinical leadership: a qualitative study exploring the views of senior healthcare leaders in the UK. Journal of the Royal Society of Medicine 2014;107(7):277–286. https://journals.sagepub.com/doi/full/10.1177/0141076814527274 Timmins N. The chief executive’s tale: views from the front line of the NHS. The King’s Fund 2016, May 10. https://www.kingsfund.org.uk/sites/default/files/field/field_publication_file/The-chief-executive-tale-Kings-Fund-May-2016.pdf Haslam SA, Reicher S, Platow MJ. (2011). New York, NY: Psychology Press. http://documents.routledge-interactive.s3.amazonaws.com/9781841696102/Sample_CHPT_9781841696102.pdf Palmer R, Cragg R, Wall D, Wilkie V. Team and leadership styles of junior doctors. International Journal of Clinical Leadership 2008;16(3):131–135. https://web.a.ebscohost.com/abstract?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=1757207X&AN=47155596&h=0FEemBZ8QKBpo9AhjNRy2qZ8EremAggmg2mmR03k36zyWniYAj2ySa8iESPpl%2fFkJMjQS6Do1GgvF6ZwOo51Gw%3d%3d&crl=c&resultNs=AdminWebAuth&resultLocal=ErrCrlNotAuth&crlhashurl=login.aspx%3fdirect%3dtrue%26profile%3dehost%26scope%3dsite%26authtype%3dcrawler%26jrnl%3d1757207X%26AN%3d47155596
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".