Psychosocial Determinants of Hand Hygiene, Facemask Wearing, and Physical Distancing During the COVID-19 Pandemic: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Hand hygiene, facemask wearing, and physical distancing play a crucial role in the prevention of the COVID-19 pandemic. Identifying the key psychosocial determinants of these precautionary behaviors contributes to effective intervention and policymaking for COVID-19 and future pandemics. PURPOSE: This study aimed to systematically review and meta-analyze available evidence on psychosocial determinants of the general population's practice of three precautionary behaviors, based on the Risk, Attitudes, Norms, Abilities, and Self-regulation (RANAS) framework. METHODS: Literature was identified by searching seven databases and relevant review papers. Observational and experimental studies targeting the general population (≥18 years) published between January 2020 to September 2021 were included. Pooled effect sizes were calculated with the inverse-variance method using random-effects models. RESULTS: A total of 51 studies (64 samples) were included in the qualitative synthesis, of which 30 studies (42 samples) were included in the meta-analysis. RANAS-based constructs including knowledge, pros attitudes, and perceived norms were identified as significant determinants of all three behaviors in the meta-analysis. Perceived susceptibility and cons attitudes showed no significant associations with any behaviors. Perceived severity, perceived control, self-efficacy, and behavioral intention were significantly associated with one or two behaviors. Country (western vs. eastern hemispheres) significantly moderated the effects of certain risk and ability factors. CONCLUSIONS: More research is needed with respect to the intention-behavior relationship, self-regulatory and reflexive factors of precautionary behaviors, as well as the exploration of the potential moderating effect of sociodemographic factors.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".