Training public health superheroes: five talents for public health leadership
Bibliographic record
Abstract
BACKGROUND: Public health leaders have been criticized for their policy stances, relationships with governments and failure to train the next generation. New approaches to the identification and training of public health leaders may be required. To inform these, lessons can be drawn from public health 'superheroes'; public health leaders perceived to be the most admired and effective by their peers. METHODS: Members and Fellows of the UK Faculty of Public Health were contacted via e-newsletter and magazine and asked to nominate their 'Public Health Superhero'. Twenty-six responses were received, nominating 40 different people. Twelve semi-structured interviews were conducted. Thematic analysis, based on 'grounded theory', was conducted. RESULTS: Five leadership 'talents' for public health were identified: mentoring-nurturing, shaping-organizing, networking-connecting, knowing-interpreting and advocating-impacting. CONCLUSIONS: Talent-based approaches have been effective for leadership development in other sectors. These talents are the first specific to the practice of public health and align with some aspects of existing frameworks. An increased focus on identifying and developing talents during public health training, as opposed to 'competency'-based approaches, may be effective in strengthening public health leadership. Further research to understand the combination and intensity of talents across a larger sample of public health leaders is required.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".