COVID-19: Role of Integrated Regional Health System Towards Controlling Pandemic in the Community, Intermediate and Long-Term Care
Bibliographic record
Abstract
Older adults at home, intermediate and long-term care (ILTC) setting including nursing home and hospice care are vulnerable to COVID-19 infection with increased morbidity and mortality. Singapore is one of the fastest aging countries in Asia where 14.4% of population is above 65 years old and this will double by 2030 (1). About 16000 older adults live in long-term care facilities and many more attend different types of day care facilities (2). Many of the residents are frail, with underlying dementia and / or multimorbidity and often present atypically causing a delay in diagnosis. In many countries, COVID-19 has spread amongst nursing home residents with mortality ranging from 24% in Hungary to 82% in Canada (3). It is known that 56% of residents may test positive while in pre-symptomatic stage, and many countries have put in initiatives to decrease the risk of spread in care homes (4). COVID-19 pandemic has highlighted the importance of communication and collaboration amongst ILTC providers which in many countries are run by non-governmental organization’s, healthcare providers, regional and national healthcare leaders.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".