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Record W3197139157 · doi:10.1177/10775587211039201

Public Health and Health Sector Crisis Leadership During Pandemics: A Review of the Medical and Business Literature

2021· review· en· W3197139157 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Care Research and Review · 2021
Typereview
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of VictoriaUniversity of Toronto
Fundersnot available
KeywordsPandemicPreparednessPublic relationsPublic healthContext (archaeology)Health carePolitical scienceCrisis managementEmpirical evidenceCoronavirus disease 2019 (COVID-19)MedicineNursingDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The global scale and unpredictable nature of the current COVID-19 pandemic have put a significant burden on health care and public health leaders, for whom preparedness plans and evidence-based guidelines have proven insufficient to guide actions. This article presents a review of empirical articles on the topics of "crisis leadership" and "pandemic" across medical and business databases between 2003 (since SARS) and-December 2020 and has identified 35 articles for detailed analyses. We use the articles' evidence on leadership behaviors and skills that have been key to pandemic responses to characterize the types of leadership competencies commonly exhibited in a pandemic context. Task-oriented competencies, including preparing and planning, establishing collaborations, and conducting crisis communication, received the most attention. However, people-oriented and adaptive-oriented competencies were as fundamental in overcoming the structural, political, and cultural contexts unique to pandemics.

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.

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)0.020
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.474
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.639
GPT teacher head0.587
Teacher spread0.052 · 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