Where Are the Healthcare Leaders? The Need for Investment in Leadership Development
Bibliographic record
Abstract
Is there a crisis in healthcare leadership? In order to understand this question we must first look at what is meant by the term leadership. We prize and admire leadership skills, yet we have little understanding of how and why some persons are more effective leaders than others. This paper describes the changing concept of leadership in the context of both corporate and healthcare settings. The approaches taken in corporate leadership development programs are contrasted with the way in which leaders have been developed in healthcare. The authors assert that there are unique characteristics of health systems and organizations that warrant a tailored approach. A new model of developing healthcare leaders is proposed, one that could transform the educational process and improve outcomes. The authors call for a "back to basics" about how adults learn and outline an approach to leadership development in healthcare that includes principles of competency-based development, interdisciplinary and team learning and continuous assessment. Their conclusion is that leadership development is not done solely to improve the leadership skills of one individual but is an essential component of the development of the organization as a whole. Progressive health systems that invest in leadership development for the entire senior management team will have the more significant return on investment in terms of organizational effectiveness.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".