Mind the public health leadership gap: the opportunities and challenges of engaging high-profile individuals in the public health agenda
Bibliographic record
Abstract
BACKGROUND: Public health leadership has been criticized as being ineffective. The public health profession is relatively small. Critics have argued that there is over-emphasis on technical aspects and insufficient use of the 'community as a source of public health actions'. METHODS: The paper analyses the resources, motivations and skills utilized by high-profile individuals who have made contributions to the public health agenda. The phenomenon of celebrity diplomacy is critiqued. Two exemplars are discussed: Jamie Oliver and Michael Bloomberg. The risks of involving celebrities are also considered. RESULTS: Leaders for public health demonstrate 'a paradoxical blend of personal humility and professional will' to make the 'right decisions happen'. While they may have ego or self-interest, in this context, at least, they channel their ambition for the public health cause, not themselves. CONCLUSIONS: Leaders from outside public health may have no understanding of what public health is nor consider their work as part of a wider public health agenda. It is important to understand why they become leaders for public health. This will inform a strategy for how others may be encouraged to collaborate for public health causes. Some key points for working with high-profile leaders for public health are identified.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".