Practice Level and Associated Factors Towards the Preventive Measures of COVID-19 Among the General Population; A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Studies conducted on the practice of COVID-19 preventive methods across the world are highly inconsistent and inconclusive. Hence, this study intended to estimate the pooled preventive practice and its determinants among the general population. Methods This study was conducted using online databases (PubMed, HINARI, Scopus, EMBASE, Science Direct, and Cochrane library database), African Journals online, Google Scholar, open gray and online repository accessed studies. The quality of the included studies was assessed using Newcastle-Ottawa Quality Assessment Scale (NOS). STATA 14.0 software for analysis. The existence of heterogeneity between studies was checked using Cochran Q test and I2 test statistics and then, the presence of publication bias was detected using both funnel plot and Egger's test. Results 51 studies were included and the pooled level of practice toward the preventive measures of COVID-19 was 74.4% (95% CI: 70.2–78.6%, I2 = 99.7%, P < 0.001] using a random effects model. Being female [OR = 1.97: 95% CI 1.75, 2.23; I2 = 0.0%, P < 0.698], rural residence [OR = 0.53: 95% CI 0.44, 0.65; I2 = 73.5%, P < 0.013], attending higher education level [OR = 1.47: 95% CI 1.18, 1.83; I2 = 75.4%, P < 0.001], being employed [OR = 2.12: 95% CI 1.44, 3.12; I2 = 91.8%, P < 0.001], age < 30 [OR = 0.73: 95% CI 0.60, 0.89; I2 = 73.9%, P < 0.001], and knowledgeable [OR = 1.22: 95% CI 1.09, 1.36; I2 = 47.3%, P < 0.077] were the independent predictors of adequate practice level. Conclusions nearly three-fourths of the general population has an adequate preventive practice level toward COVID-19. Thus, the global, regional, national, and local governments need to establish policies and strategies to address the identified 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 | 0.026 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.011 | 0.010 |
| 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".