Impact and variability of social determinants of health on the transmission and outcomes of COVID-19 across the world: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has exacerbated health inequalities across the globe, disproportionately affecting those with poor social determinants of health (SDOHs). It is imperative to understand how SDOH influences the transmission and outcomes (positive case, hospitalisation and mortality) of COVID-19. This systematic review will investigate the impact of a wide range of SDOHs across the globe on the transmission and outcomes of COVID-19. METHODS AND ANALYSIS: This review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol guidelines. We will search three electronic bibliographical databases (MEDLINE via PubMed, Embase and Scopus), as well as the WHO COVID-19 Global Research on Coronavirus Disease database. We will consider observational studies that report statistical relationships between the SDOHs (as listed in PROGRESS-Plus and Healthy People 2020) and COVID-19 transmission and outcomes. There will be no limitation on the geographical location of publications. The quality of included observational studies will be assessed using a modified version of the Newcastle-Ottawa Scale. A narrative synthesis without meta-analysis reporting standards will be used to report the review findings. ETHICS AND DISSEMINATION: This review will be based on published studies obtained from publicly available sources, and therefore, ethical approval is not required. We will publish the results of this review in a peer-reviewed journal, as well as present the study findings at a national conference. PROSPERO REGISTRATION NUMBER: CRD42021228818.
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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 | 0.116 | 0.122 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.017 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.076 | 0.012 |
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".