Identifying Health Equity Factors That Influence the Public’s Perception of COVID-19 Health Information and Recommendations: A Scoping Review
Bibliographic record
Abstract
The COVID-19 pandemic has impacted global public health and public trust in health recommendations. Trust in health information may waver in the context of health inequities. The objective of this scoping review is to map evidence on public perceptions of COVID-19 prevention information using the PROGRESS-Plus health equity framework. We systematically searched the MEDLINE, Cochrane Central Register of Controlled Trials, PsycInfo, and Embase databases from January 2020 to July 2021. We identified 792 citations and 31 studies published in 15 countries that met all inclusion criteria. The majority (30/31; 96.7%) of the studies used an observational design (74.2% cross-sectional, 16.1% cohort, 6.5% case study, 3.2% experimental trials). Most studies (61.3%) reported on perception, understanding, and uptake, and 35.5% reported on engagement, compliance, and adherence to COVID-19 measures. The most frequently reported sources of COVID-related information were social media, TV, news (newspapers/news websites), and government sources. We identified five important equity factors related to public trust and uptake of recommendations: education and health literacy (19 studies; 61.3%), gender (15 studies; 48.4%), age (15 studies; 48.4%), socioeconomic status (11 studies; 35.5%), and place of residence (10 studies; 32.3%). Our review suggests that equity factors play a role in public perception of COVID-19 information and recommendations. A future systematic review could be conducted to estimate the impact of equity factors on perception and behavior outcomes.
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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 | 0.037 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".