Factors influencing mask use and physical distancing for COVID-19: A qualitative evidence synthesis
Bibliographic record
Abstract
Background: The World Health Organization (WHO) recommends a bundle of precautions to reduce community transmission of COVID-19, including mask use and physical distancing. However, there is evidence that suggests poor adherence to these health measures community settings. Aim: To summarise qualitative research evidence on the perceptions and factors influencing masks use and physical distancing in the context of the COVID-19 pandemic. Setting: We included studies conducted in community settings. Method: An electronic database search was conducted using search terms derived from the inclusion criteria and combined in a peer-reviewed search strategy. Thirty studies were sampled. Qualitative data analysis was performed using the thematic synthesis approach. The confidence in each review finding was ascertained using the Grading of Recommendations, Assessment, Development and Evaluations - Confidence in the evidence from Reviews of Qualitative Research (GRADE-CERQual) approach. Results: Ten analytical themes of low to high confidence were identified. Values, belief systems and cultural norms shaped the perception and uptake of mask use and physical distancing. Key barriers included the cost of masks, limited infrastructure for spatial separation and inconsistent political or government messaging, while visual cues and social responsibility facilitated adherence. Conclusion: Personal values and preferences influenced individuals' adherence to these public health measures. Political or government messaging is important to aid understanding and adherence. Contribution: Insights provided by this synthesis can support future emergency preparedness and response to outbreaks of acute respiratory infections by providing policy makers with information needed to make contextually relevant recommendations to enhance adherence.
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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 | 0.065 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".