Reduction of MERS-CoV Transmission among Healthcare Workers and Patients in Saudi Arabian Healthcare Settings: A Scoping Review
Bibliographic record
Abstract
Background No review consolidating available evidence of the various interventions for preventing MERS-CoV transmission in healthcare settings has been published to inform practice. The MERS-CoV outbreak in Saudi Arabia led to wide-scale hospitalisations and, among other individuals at risk, healthcare workers (HCW) were one of the most affected groups. This study evaluates the effectiveness of various interventions implemented to prevent MERS-CoV transmission to HCW and MERS-negative patients in Saudi Arabian healthcare settings. Methods This review summarises and evaluates the effectiveness of MERS-CoV infection prevention and control (IPC) measures in Saudi Arabian hospital settings. Instead of using ‘best quality/evidence’ studies, the review has included as many relevant studies as possible. Results Various IPC measures were deemed effective. However, since no analysis of their effectiveness had been undertaken, it was not possible to determine the interventions’ level of effectiveness as applied in hospital settings. The studies appeared to rely on the assumption that the extent of MERS-CoV transmission control observed was a direct reflection of the implemented IPC measures. Conclusions Robust studies, using empirical methods, should be conducted to measure the effectiveness of the various IPC measures developed and implemented to control MERS-CoV transmission
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".