Middle East respiratory syndrome coronavirus (<scp>MERS‐CoV</scp>) infection: Analyses of risk factors and literature review of knowledge, attitude and practices
Bibliographic record
Abstract
The study aimed to explore the risk factors for MERS-CoV infection and systematic review of knowledge, attitudes and practices (KAP) with regard to MERS-CoV among the health care workers (HCWs) and the general population. The World Health Organization's MERS-CoV line list (January 2013-January 2020) of the Kingdom of Saudi Arabia (KSA) was analysed. A Poisson regression model was used to calculate the univariate relative risk of outcomes to each potential risk factor, p-values and 95% confidence intervals. An electronic literature search was conducted to assess knowledge, attitudes and practices of the HCWs and general population of the KSA, with regards to transmission of the infection, risk factors and preventative measures. The line list analysis shows that age, gender, comorbidity, exposure to camels and camel milk consumption were associated with an increased risk of fatality; however, year-wise analysis did not show any decline. Over the years, the mean durations between the symptom onset and hospitalization; the hospitalization and laboratory confirmation have reduced. The review of literature shows that the health care workers and the general population had inadequate knowledge about MERS-CoV, lacked motivation and were disconnected from the health authorities. The WHO line list provides information on risk factors for MERS-CoV, KAP analysis helps to know the potential underlying factors. The literature review shows that continuous education for HCWs and increasing public awareness can help effectively manage future MERS-CoV.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".