The Prevalence of COVID-19 in Al Qassim Province -Saudi Arabia: A Sociodemographic Analysis
Bibliographic record
Abstract
BACKGROUND: The resurgence of COVID-19 cases in Saudi Arabia, despite ongoing control measures, warrants further analysis. AIM: We aimed to investigate the prevalence and sociodemographic risk factors of COVID-19 in Al Qassim Province, Saudi Arabia. We explored perceptions toward vaccination, social isolation and community adherence to social distancing measures. METHODS: We collected data reported by the Weqaya, Saudi Centre for Disease Prevention and Control, and conducted a cross-sectional study within the province. RESULTS: Up to 9 May 2021, 15 497 positive cases were detected, comprising a prevalence of 1.46%. Uyun Al-Jiwa and Riyadh Al-Khabra had the highest infection rate. Our sample of 511 participants revealed an exposure rate of 52.1% (n=266) with no specific sociodemographic risk factor. Self-isolation following exposure to a confirmed or probable case occurred less among older age groups and married participants, and was not influenced by the presence of underlying chronic diseases. The majority of participants perceived community compliance with social distancing measures to fall within the ‘sometimes’ category. Finally, the vaccination acceptance rate was 72.6% (n=371). CONCLUSION: The findings of the current study emphasise the need to adopt further measures to encourage adherence to social distancing and self-isolation, especially among vulnerable groups.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".