Reducing Morbidity and Mortality Rates from COVID-19, Influenza and Pneumococcal Illness in Nursing Homes and Long-Term Care Facilities by Vaccination and Comprehensive Infection Control Interventions
Bibliographic record
Abstract
The COVID-19 pandemic identifies the problems of preventing respiratory illnesses in seniors, especially frail multimorbidity seniors in nursing homes and Long-Term Care Facilities (LCTFs). Medline and Embase were searched for nursing homes, long-term care facilities, respiratory tract infections, disease transmission, infection control, mortality, systematic reviews and meta-analyses. For seniors, there is strong evidence to vaccinate against influenza, SARS-CoV-2 and pneumococcal disease, and evidence is awaited for effectiveness against COVID-19 variants and when to revaccinate. There is strong evidence to promptly introduce comprehensive infection control interventions in LCFTs: no admissions from inpatient wards with COVID-19 patients; quarantine and monitor new admissions in single-patient rooms; screen residents, staff and visitors daily for temperature and symptoms; and staff work in only one home. Depending on the vaccination situation and the current risk situation, visiting restrictions and meals in the residents' own rooms may be necessary, and reduce crowding with individual patient rooms. Regional LTCF administrators should closely monitor and provide staff and PPE resources. The CDC COVID-19 tool measures 33 infection control indicators. Hand washing, social distancing, PPE (gowns, gloves, masks, eye protection), enhanced cleaning of rooms and high-touch surfaces need comprehensive implementation while awaiting more studies at low risk of bias. Individual ventilation with HEPA filters for all patient and common rooms and hallways is needed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".