A rapid review of equity considerations in large-scale testing campaigns during infectious disease epidemics
Bibliographic record
Abstract
ABSTRACT Context Large-scale testing is an intervention that is instrumental for infectious disease control and a central tool for the COVID-19 pandemic. Our rapid review aimed to identify if and how equity has been considered in large-scale testing initiatives. Methods We searched Web of Science and PubMed in November 2020 and followed PRISMA recommendations for scoping reviews. Articles were analyzed using descriptive and thematic analysis. Results Our search resulted in 291 studies of which 41 were included for data extraction after full article screening. Most of the included articles (83%) reported on HIV-related screening programs, while the remaining programs focused on other sexually transmitted infections (n=3) or COVID-19 (n=4). None of the studies presented a formal definition of (in)equity in testing, however, 23 articles did indirectly include elements of equity in the program or intervention design, largely through the justification of their target population. Conclusion The studies included in our rapid review did not explicitly consider equity in their design or evaluation. It is imperative that equity is incorporated into the design of infectious disease testing programs and serves as an important reminder of how equity considerations are needed for SARS-CoV-2 testing and vaccination programs.
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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 | 0.020 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".