A framework for identifying and mitigating the equity harms of COVID-19 policy interventions
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) is a global pandemic. Governments have implemented combinations of "lockdown" measures of various stringencies, including school and workplace closures, cancellations of public events, and restrictions on internal and external movements. These policy interventions are an attempt to shield high-risk individuals and to prevent overwhelming countries' healthcare systems, or, colloquially, "flatten the curve." However, these policy interventions may come with physical and psychological health harms, group and social harms, and opportunity costs. These policies may particularly affect vulnerable populations and not only exacerbate pre-existing inequities but also generate new ones. METHODS: We developed a conceptual framework to identify and categorize adverse effects of COVID-19 lockdown measures. We based our framework on Lorenc and Oliver's framework for the adverse effects of public health interventions and the PROGRESS-Plus equity framework. To test its application, we purposively sampled COVID-19 policy examples from around the world and evaluated them for the potential physical, psychological, and social harms, as well as opportunity costs, in each of the PROGRESS-Plus equity domains: Place of residence, Race/ethnicity, Occupation, Gender/sex, Religion, Education, Socioeconomic status, Social capital, Plus (age, and disability). RESULTS: We found examples of inequitably distributed adverse effects for each COVID-19 lockdown policy example, stratified by a low- or middle-income country and high-income country, in every PROGRESS-Plus equity domain. We identified the known policy interventions intended to mitigate some of these adverse effects. The same harms (anxiety, depression, food insecurity, loneliness, stigma, violence) appear to be repeated across many groups and are exacerbated by several COVID-19 policy interventions. CONCLUSION: Our conceptual framework highlights the fact that COVID-19 policy interventions can generate or exacerbate interactive and multiplicative equity harms. Applying this framework can help in three ways: (1) identifying the areas where a policy intervention may generate inequitable adverse effects; (2) mitigating the policy and practice interventions by facilitating the systematic examination of relevant evidence; and (3) planning for lifting COVID-19 lockdowns and policy interventions around the world.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.022 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".