Public health leadership in the COVID-19 era: how does it fit? A scoping review
Bibliographic record
Abstract
The COVID-19 pandemic has put a lot of pressure on all the world's health systems and public health leaders who have often found themselves unprepared to handle an emergency of this magnitude. This study aims to bring together published evidence on the qualities required to leaders to deal with a public health issue like the COVID-19 pandemic. This scoping literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. A search of relevant articles was performed in the PubMed, Scopus and Web of Science databases. A total of 2499 records were screened, and 45 articles were included, from which 93 characteristics of effective leadership were extrapolated and grouped into 6 clusters. The qualities most frequently reported in the articles were human traits and emotional intelligence (46.7%) and communication skills such as transparency and reliability (48.9%). Responsiveness and preparedness (40%), management skills (33.3%) and team working (35.6%) are considered by a significant percentage of the articles as necessary for the construction of rapid and effective measures in response to the emergency. A considerable proportion of articles also highlighted the need for leaders capable of making evidence-based decisions and driving innovation (31.1%). Although identifying leaders who possess all the skills described in this study appears complex, determining the key characteristics of effective public health leadership in a crisis, such as the COVID-19 pandemic, is useful not only in selecting future leaders but also in implementing training and education programmes for the public health workforce.
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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 | 0.052 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| 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".