Cultivating the next generation of healthcare leaders: reflections from an established healthcare leader
Bibliographic record
Abstract
BACKGROUND: Dr Andrea Doria is Professor and Vice-Chair of Radiology (Clinical Practice Improvement) at the University of Toronto, Research Director, Senior Scientist and Imaging Lead of Personalised Child Health, The Hospital for Sick Children (SickKids), Toronto, Canada. Over the past few decades, Dr Doria has established a track record of healthcare leadership. Based on Dr Doria's extensive leadership experience, she believes it is essential for established healthcare leaders to be involved in cultivating emerging healthcare leaders. METHODS: An interview was conducted with Dr Doria to learn about key lessons she believes are essential for healthcare leaders to help develop the next generation. Dr Doria reflected on her leadership style and experiences, sharing what has worked to improve the effectiveness of her teams. RESULTS: Key messages were reflected upon, including practical ways for senior leaders to support the next generation; leadership insights gained from the pandemic; the importance of building diversity in teams and nurturing leaders from underrepresented minorities; challenges to be aware of for the future of healthcare leadership; finding inspiration from team members and essential traits for healthcare leaders. CONCLUSION: Through cultivating the next generation of healthcare leaders, established leaders can be involved in establishing a brighter future for healthcare. This article describes reflections and practical takeaways that can help established leaders support emerging leaders and build their leadership skills.
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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 | 0.031 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.035 |
| Insufficient payload (model declined to judge) | 0.002 | 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".