Reconceptualizing successful pandemic preparedness and response: A feminist perspective
Bibliographic record
Abstract
Pandemic preparedness and COVID-19 response indicators focus on public health outcomes (such as infections, case fatalities, and vaccination rates), health system capacity, and/or the effects of the pandemic on the economy, yet this avoids more political questions regarding how responses were mobilized. Pandemic preparedness country rankings have been called into question due to their inability to predict COVID-19 response and outcomes, and COVID-19 response indicators have ignored one of the most well documented secondary effects of the pandemic - its disproportionate effects on women. This paper analyzes pandemic preparedness and response indicators from a feminist perspective to understand how indicators might consider the secondary effects of the pandemic on women and other equity deserving groups. Following a discussion of the tensions that exist between feminist methodologies and the reliance on indicators by policymakers in preparing and responding to health emergencies, we assess the strengths and weakness of current pandemic preparedness and COVID-19 response indicators. The risk with existing pandemic preparedness and response indicators is that they give only limited attention to secondary effects of pandemics and inequities in terms of who is disproportionately affected. There is an urgent need to reconceptualize what 'successful' pandemic preparedness and response entails, moving beyond epidemiological and economic measurements. We suggest how efforts to design COVID response indicators on gender inclusion could inform pandemic preparedness and associated indicators.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".