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Record W3216770342 · doi:10.1136/bmj-2021-067512

Assessing the role of qualitative factors in pandemic responses

2021· article· en· W3216770342 on OpenAlexaff
Melisa Mei Jin Tan, Rachel Neill, Victoria Haldane, Anne-Sophie Jung, Chuan De Foo, See Mieng Tan, Pami Shrestha, Monica Verma, Mathias Bonk, Salma M. Abdalla, Helena Legido‐Quigley

Bibliographic record

VenueBMJ · 2021
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPreparednessGovernment (linguistics)PandemicWork (physics)BusinessPublic relationsGlobal healthEconomic growthPolitical scienceHealth careMedicineCoronavirus disease 2019 (COVID-19)EconomicsInfectious disease (medical specialty)DiseaseEngineering

Abstract

fetched live from OpenAlex

Melisa Tan and colleagues argue that assessments of national pandemic preparedness and response capacities should be extended to include social factors, leadership, and use of evidence The covid-19 pandemic has emphasised shortcomings in global systems for measuring the ability of countries to respond rapidly to emerging infectious disease threats. Existing monitoring and evaluation processes—including the Global Health Security Index, the Epidemic Preparedness Index, and the Joint External Evaluation—have highlighted a lack of preparedness across countries, particularly among low and middle income countries. Currently, these measurement frameworks emphasise structural aspects of the health system, including capacity, laboratory infrastructure, financing, surveillance, and emergency response operations, but fail to account for critical dimensions such as governance, cooperation, and collaboration, which are difficult to quantify.12 During the covid-19 pandemic these qualities have shaped the ability of countries to prepare proactively, respond promptly, and recover equitably while protecting the health and wellbeing of communities. Overlooking these considerations has biased existing frameworks towards high income countries, failed to account for socioeconomic inequalities, and underestimated the role of leadership and societal engagement in translating policy planning into implementation.12 The preparedness indicators therefore do not accurately reflect the ability of countries to respond to infectious hazards. After consulting experts on covid-19 working across academia, government, the private sector, and not-for-profit sector, and carrying out desk reviews (see supplementary data on bmj.com for details),1 and building on recent work that highlighted limitations of the Global Health Security Index, we outline six factors that have shaped national responses to covid-19 (box 1). Such “soft” factors must be incorporated into future national and global measurement frameworks to advance a holistic, equitable, and truly preventive approach to evaluating pandemic preparedness. Box 1 ### Essential components in shaping pandemic preparedness and responseRETURN TO TEXT

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.126

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.201
GPT teacher head0.539
Teacher spread0.338 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations27
Published2021
Admission routes1
Has abstractyes

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