Assessing the role of qualitative factors in pandemic responses
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".