Learning leaders lead learning: Exploring future healthcare leadership competencies in the context of change
Bibliographic record
Abstract
This abstract explores competences required of healthcare leaders in the face of rapid global change. Trends affecting the way we live and work include technological advancements, demographic shifts, geopolitical dynamics, a weakened world economy, and ecological sustainability. Healthcare leaders are increasingly challenged to adapt to these multifaceted changes, emphasizing the need for transformative leadership and a culture of continuous learning. The study explores the competences necessary for healthcare leaders in welfare countries, focusing on Northern Europe, Australia and Canada, to address these evolving demands, especially focusing on analyzing post-Covid articles for our result. It highlights the central role of updated leadership competences to guarantee patient safety and fostering workforce health and satisfaction. As the world continues to evolve, the healthcare field must adapt, necessitating leaders with a broad skill set that includes leading oneself, engaging others, achieving results, developing coalitions, system transformation, and the ability to navigate complexity. The study's primary objectives are to clarify the importance of a learning culture for the future of healthcare and identify the essential competences required for healthcare leaders to cultivate such a culture. The research employs a literature review with deductive content analysis to answer these questions and provide insights for further research.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".