COVID-19, frailty and long-term care: Implications for policy and practice
Bibliographic record
Abstract
Older adults have been disproportionately affected by the COVID-19 pandemic, with many outbreaks occurring in Long Term Care Facilities (LTCFs). We discuss this vulnerability among LTCF residents using an ecological framework, on levels spanning from the individual to families and caregivers, institutions, health services and systems, communities, and contextual government policies. Challenges abound for fully understanding the burden of COVID-19 in LTCF, including differences in nomenclature, data collection systems, cultural differences, varied social welfare models, and (often) under-resourcing of the LTC sector. Registration of cases and deaths may be limited by testing capacity and policy, record-keeping and reporting procedures. Hospitalization and death rates may be inaccurate depending on atypical presentations and whether or not residents' goals of care include escalation of care and transfer to hospital. Given the important contribution of frailty, use of the Clinical Frailty Scale (CFS) is discussed as a readily implementable measure, as are lessons learned from the study of frailty in relation to influenza. Biomarkers hold emerging promise in helping to predict disease severity and address the puzzle of why some frail LTCF residents are resilient to COVID-19, either remaining test-negative despite exposure or having asymptomatic infection, while others experience the full range of illness severity including critical illness and death. Strong and coordinated surveillance and research focused on LTCFs and their frail residents is required. These efforts should include widespread assessment of frailty using feasible and readily implementable tools such as the CFS, and rigorous reporting of morbidity and mortality in LTCFs.
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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 | 0.023 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 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".