Distinct journeys and unique stories: how individuals from multiple professions cultivate careers in healthcare leadership
Bibliographic record
Abstract
INTRODUCTION: or should be in these roles, or how more diverse groups of professionals navigate the journey into these roles. We sought to interview leaders from multiple professional backgrounds to understand how their career journey led them to their executive role. METHODS: We purposively sampled Canadian hospital executives with diverse professional and educational backgrounds. Through semistructured interviews, we explored their individual leadership journeys, and their experiences working with others along the way. Our team worked together to analyse data using a phenomenographic approach. RESULTS: Fourteen executive-level leaders from diverse professional backgrounds were interviewed. Overall, we noted three main trajectories for people to become hospital leaders: the achievement journey, the unexpected journey and the practical journey. These journeys corresponded to three main identities the champion leader, the discovered leader and the pragmatic leader, respectively. We found that some individuals had multiple trajectories and identities. CONCLUSIONS: Improved diversity in executive hospital leadership may support transformational change in healthcare; however, this promise may not be automatically realised. Critical reflection on current hiring processes, career development and mentorship is warranted to support those with diverse and distinct backgrounds to enter and thrive in these roles.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".