COVID-19 Economic Response and Recovery: A Rapid Scoping Review
Bibliographic record
Abstract
This rapid scoping review of existing evidence and research gaps addressed the following question: what research evidence exists and what are the research gaps at global, regional, and national levels on interventions to protect jobs, small- and medium-sized enterprises, and formal/informal sector workers in socioeconomic response to the coronavirus disease 2019 (COVID-19) pandemic? The results are based on 79 publications deemed eligible for inclusion after the screening and prioritizing of 1,658 records. The findings are organized according to the 3 main categories of socioeconomic interventions-protecting jobs, enterprises, and workers-although the 3 are intertwined. Most results were derived from global-level gray literature with recommendations for interventions and implicit links to the sustainable development goals. Based on research gaps uncovered in the review, future implementation science research needs to focus on designing, implementing, evaluating, and scaling: effective evidence-based socioeconomic interventions; equity-focused, redistributive, and transformative interventions; comprehensive packages of complementary interventions; interventions to upend root causes of systemic social inequities; collaborative and participatory approaches; interventions that integrate environmental sustainability; and city-level interventions. Failing to consider the environmental dimensions of economic recovery is shortsighted and will ultimately exacerbate social inequities and poverty and undermine economic stability in the long term.
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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 | 0.072 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.037 | 0.029 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".