The Impact of COVID‐19 Pandemic on Long‐Term Care Facilities Worldwide: An Overview on International Issues
Bibliographic record
Abstract
The COVID-19 pandemic had a great negative impact on nursing homes, with massive outbreaks being reported in care facilities all over the world, affecting not only the residents but also the care workers and visitors. Due to their advanced age and numerous underlying diseases, the inhabitants of long-term care facilities represent a vulnerable population that should benefit from additional protective measures against contamination. Recently, multiple countries such as France, Spain, Belgium, Canada, and the United States of America reported that an important fraction from the total number of deaths due to the SARS-CoV-2 infection emerged from nursing homes. The scope of this paper was to present the latest data regarding the COVID-19 spread in care homes worldwide, identifying causes and possible solutions that would limit the outbreaks in this overlooked category of population. It is the authors' hope that raising awareness on this matter would encourage more studies to be conducted, considering the fact that there is little information available on the impact of the SARS-CoV-2 pandemic on nursing homes. Establishing national databases that would register all nursing home residents and their health status would be of great help in the future not only for managing the ongoing pandemic but also for assessing the level of care that is needed in this particularly fragile setting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".