MétaCan
Menu
Back to cohort
Record W2166580648 · doi:10.1093/pubmed/fdu003

Mind the public health leadership gap: the opportunities and challenges of engaging high-profile individuals in the public health agenda

2014· article· en· W2166580648 on OpenAlexaff
Darren Shickle, Matthew Day, Kevin Smith, Ken Zakariasen, Jacob Moskol, Thomas R. Oliver

Bibliographic record

VenueJournal of Public Health · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPublic healthPublic relationsContext (archaeology)International healthHealth promotionHealth policyPolitical scienceSociologyMedicineNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.134
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.764
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1340.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.707
GPT teacher head0.487
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations5
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Public HealthSame topicPublic Health Policies and EducationFrench-language works237,207