Assessment of health equity consideration in masking/PPE policies to contain COVID-19 using PROGRESS-plus framework: a systematic review
Bibliographic record
Abstract
INTRODUCTION: There is increasing evidence that COVID-19 has unmasked the true magnitude of health inequity worldwide. Policies and guidance for containing the infection and reducing the COVID-19 related deaths have proven to be effective, however the extent to which health inequity factors were considered in these policies is rather unknown. The aim of this study is to measure the extent to which COVID-19 related policies reflect equity considerations by focusing on the global policy landscape around wearing masks and personal protection equipment (PPE). METHODS: A systematic search for published documents on COVID-19 and masks/PPE was conducted across six databases: PubMed, EMBASE, CINAHL, ERIC, ASSIA and Psycinfo. Reviews, policy documents, briefs related to COVID-19 and masks/PPE were included in the review. To assess the extent of incorporation of equity in the policy documents, a guidance framework known as 'PROGRESS-Plus': Place of residence, Race/ethnicity, Occupation, Gender/sex, Religion, Education, Socioeconomic status, Social capital, Plus (age, disability etc.) was utilized. RESULTS: This review included 212 policy documents. Out of 212 policy documents, 190 policy documents (89.62%) included at least one PROGRESS-plus component. Most of the policy documents (n = 163, 85.79%) focused on "occupation" component of the PROGRESS-plus followed by personal characteristics associated with discrimination (n = 4;2.11%), place of residence (n = 2;1.05%) and education (n = 1;0.53%). Subgroup analysis revealed that most of the policy documents (n = 176, 83.01%) were focused on "workers" such as healthcare workers, mortuary workers, school workers, transportation workers, essential workers etc. Of the remaining policy documents, most were targeted towards whole population (n = 30; 14.15%). Contrary to "worker focused" policy documents, most of the 'whole population focused' policy documents didn't have a PROGRESS-plus equity component rendering them equity limiting for the society. CONCLUSION: Our review highlights even if policies considered health inequity during the design/implementation, this consideration was often one dimensional in nature. In addition, population wide policies should be carefully designed and implemented after identifying relevant equity related barriers in order to produce better outcomes for the whole society.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".