Competencies for Transformational Leadership in Public Health—An International Delphi Consensus Study
Bibliographic record
Abstract
Objectives: This Delphi study intended to develop competencies for transformational leadership in public health, including behavioral descriptions (descriptors) tailored to individuals and their contexts. Methods: The study involved five rounds, including online “e-Delphi” consultations and real-time online workshops with experts from diverse sectors. Relevant competencies were identified through a literature review, and experts rated, ranked, rephrased, and proposed descriptors. The study followed the Guidance on Conducting and REporting DElphi Studies (CREDES) and the COmpeteNcy FramEwoRk Development in Health Professions (CONFERD-HP) reporting guidelines. Results: Our framework comprises ten competencies for transformational public health leadership (each with its descriptors) within four categories, and also describes a four-stage model for developing relevant competencies tailored to different contexts. Conclusion: Educators responsible for curriculum design, particularly those aiming to align curricula with local goals, making leadership education context-specific and -sensitive, may benefit from the proposed framework. Additionally, it can help strengthen links between education and workforce sectors, address competency gaps, and potentially reduce the out-migration of graduates in the health professions.
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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 | 0.089 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".