Predictors of attitudes and adherence to COVID-19 public health guidelines in Western countries: a rapid review of the emerging literature
Bibliographic record
Abstract
BACKGROUND: Physical distancing, wearing face masks and hand hygiene are evidence-based methods to protect the public from coronavirus disease 2019 (COVID-19) infection. There has been a proliferation of research examining characteristics that can be targeted by public health interventions. This rapid review sought to identify predictors of attitudes toward and adherence to COVID-19 public health guidelines, and identify interventions aiming to improve adherence. METHODS: Articles were retrieved from multiple databases (e.g. MEDLINE, CINAHL and medRxiv) on 6 August 2020. Studies were limited to samples collected from Western countries. Studies were classified according to the types of factor (s) examined as independent variables. The consistency of evidence for each factor was scored by two reviewers. RESULTS: In total, 1323 unique articles were identified in the initial search, resulting in 29 studies in the final synthesis. The available evidence suggests individuals who are older, identify as women, trust governments, perceive COVID-19 as threatening and access information through traditional news media are more likely to adhere with COVID-19 public health guidelines. Interventions for improving adherence have not yet been investigated thoroughly, and this review identified only three experimental studies. CONCLUSIONS: This review has identified several characteristics that impact attitudes and adherence to COVID-19 public health guidelines.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".