Charting current evidence on the health and non-health benefits and equity impacts of pandemic/epidemic individual-level economic relief programmes: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The emergence of a regional or global scale infectious disease outbreak often requires the implementation of economic relief programmes in affected jurisdictions to sustain societal welfare and, presumably, population health. While economic relief programmes are considered essential during a regional or global health crisis, there is no clear consensus in the literature about their health and non-health benefits and their impact on promoting equity. Thus, our objective is to map the current state of the literature with respect to the types of individual-level economic relief programmes implemented during infectious disease outbreaks and the impact of these programmes on the effectiveness of public health measures, individual and population health, non-health benefits and equity. METHODS AND ANALYSIS: Our scoping review is guided by the updated Arksey and O'Malley scoping review framework. Eligible studies will be identified in eight electronic databases and grey literature using text words and subject headings of the different pandemic and epidemic infectious diseases that have occurred, and economic relief programmes. Title and abstract screening and full-text screening will be conducted independently by two trained study reviewers. Data will be extracted using a pretested data extraction form. The charting of the key findings will follow a thematic narrative approach. Our review findings will provide in-depth knowledge on whether and how benefits associated with pandemic/epidemic individual-level economic relief programmes differ across social determinants of health factors.This information is critical for decision-makers as they seek to understand the role of pandemic/epidemic economic mitigation strategies to mitigate the health impact and reduce inequity gap. ETHICS AND DISSEMINATION: Since the scoping review methodology aims to synthesise evidence from literature, this review does not require ethical approval. Findings of our review will be disseminated to health stakeholders at policy meetings and conferences; published in a peer-review scientific journal; and disseminated on various social media platforms.
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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 | 0.194 | 0.236 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.041 | 0.036 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.014 |
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".