Vaccines in Long-Term Care Settings: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Older people living in long-term care facilities represent a particularly vulnerable segment of the population, who disproportionately bear the burden of infectious diseases, as recently highlighted by the COVID-19 pandemic. SUMMARY: Older long-term care residents typically cumulate several risk factors for infection and experience serious life-threatening outcomes once infected. These common infections are often compounded by the collective living environment, where it is more difficult to contain the spread of infection. Moreover, the staff may represent an additional reservoir of potential infection and mode of transmission. In this paper, we review the burden of infectious respiratory diseases in residents in long-term care and discuss the potential gains from higher vaccine coverage in this older and most vulnerable population but also from higher vaccine coverage among the facility staff. We highlight the compelling need to integrate specific vaccine recommendations for residents of long-term care into national vaccination schedules, as well as the need to include vaccination campaigns in routine protocols for infection control. Surveillance, reporting, hygiene, and individual protective measures remain key aspects in basic infection control, both in ordinary times and during epidemics. KEY MESSAGE: Vaccination of residents in long-term care facilities against respiratory diseases including influenza, pneumococcal disease, pertussis, and COVID is a simple, inexpensive, and effective means to reduce the burden of infection in this segment of the population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".