COVID-19 Perceptions Among Communities Living on Ground Crossings of Somali Region of Ethiopia: Community Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: The COVID-19 pandemic has profoundly affected the movement of people across borders in Eastern and Southern Africa. The implementation of border closures and restrictive measures has disrupted the region's economic and social dynamics. In areas where national authorities lack full control over official and unofficial land crossings, enforcing public health protocols to mitigate health risks may prove challenging. Objective: This study aimed to assess perceived factors that influence the spread and control of COVID-19 among Somali communities living on and near ground crossings in Tog Wajaale, Somali region, Ethiopia. Methods: A community-based cross-sectional study was conducted using a multistage sampling technique. Beliefs and perceptions of the virus's spread and control were partially adapted from the World Health Organization (WHO) resources, exploring four main perception themes: (1) perceived facilitators for the spread of the virus, (2) perceived inhibitors, (3) risk labeling, and (4) sociodemographic variables. A sample size of 634 was determined using the single proportion formula. Standardized mean scores (0-100) and SDs categorized perception themes, with mean differences by sociodemographic variables analyzed using ANOVA and t tests. Statistical significance was established with a 95% CI and a P value below .05. The data were analyzed using STATA version 14.1. Results: Factors influencing COVID-19 spread and control include behavioral nonadherence and enabling environments. A total of 81.9% (439/536) did not comply with social distancing, and 92.2% (493/536) faced constraints preventing them from staying home and enabling environments. Misconceptions were prevalent, including beliefs about hot weather (358/536, 66.8%), traditional medicine (36/536, 6.7%), and religiosity (425/536, 79.3%). False assurances also contributed, such as feeling safe due to geographic distance from hot spots (76/536, 14.2%) and perceiving the virus as low-risk or exaggerated (162/536, 30.2%). Only 25.2% (135/536) followed standard precautions and 29.9% (160/536) were vaccinated. Employment, region, income, sex, education, and information sources significantly influenced behavioral nonadherence, myth prevalence, and false assurances. Conclusions: The findings highlight the need for substantial risk communication and community engagement. Only 46.6% (250/536) of individuals adhered to precautionary measures, there was a high perception of nonadherence, and essential COVID-19 resources were lacking. Additionally, numerous misconceptions and false reassurances were noted. Understanding cross-border community behavior is crucial for developing effective, contextually appropriate strategies to mitigate COVID-19 risk in these regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".