Non-Pharmaceutical Interventions on COVID-19 in Workers and Residents of Nursing Homes in Geneva: A Mixed Qualitative and Quantitative Study
Bibliographic record
Abstract
The objective of this study was to examine the impact of varying levels of non-pharmaceutical interventions (NPIs) on COVID-19 transmission in nursing homes during the first wave of the pandemic. Background/Objectives: The primary aim involved exploring qualitative insights from staff and management regarding the implementation of NPIs. The secondary aim was to determine the cumulative incidence of PCR-confirmed COVID-19 cases among residents. Incident rate ratios (IRRs) were the calculated levels of NPI restrictiveness. Methods: We used a mixed methodology to identify factors that might have affected COVID-19 expansion in nursing homes in the canton of Geneva, Switzerland. For the qualitative component, we interviewed the Attending Physicians and/or Director of each nursing home. In the quantitative component, we calculated incident rate ratios (IRRs) for infection between the three levels of COVID-19-related measures taken in these nursing homes, and the cumulative incidence of PCR-confirmed COVID-19 cases in their resident population. This study was conducted in 12 nursing homes located in the canton of Geneva, Switzerland, between 1 March 2020, and 1 June 2020. Results: Most nursing homes mandated NPIs for their staff and residents during the first wave of COVID-19. We found an equal distribution of maximally (n = 4), moderately (n = 4), and minimally (n = 4) restrictive NPIs for nursing home workers and residents. The extent of NPIs implemented was not shown to be significantly associated with the cumulative incidence of COVID-19 cases among residents (maximally restrictive IRR = 3.90, 95%CI 0.82–45.54, p = 0.184; moderately restrictive IRR = 3.55, 95%CI 0.75–41.42, p = 0.212; minimally restrictive IRR = reference). Conclusions: Nursing homes in our study showed high variability in which NPIs, and to what extent, they implemented, with no significant relationship between the restrictiveness of NPIs and COVID-19 incidence among nursing home residents. This suggests that other factors influence the transmission of COVID-19 in these settings. Future research should explore additional determinants and the balance between strict NPIs and the overall well-being of residents.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".