Frailty and the impacts of the COVID-19 pandemic on community-living middle-aged and older adults: an analysis of data from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
BACKGROUND: frailty imparts a higher risk for hospitalisation, mortality and morbidity due to COVID-19 infection, but the broader impacts of the pandemic and associated public health measures on community-living people with frailty are less known. METHODS: we used cross-sectional data from 23,974 Canadian Longitudinal Study on Aging participants who completed a COVID-19 interview (Sept-Dec 2020). Participants were included regardless of whether they had COVID-19 or not. They were asked about health, resource, relationship and health care access impacts experienced during the pandemic. Unadjusted and adjusted prevalence of impacts was estimated by frailty index quartile. We further examined if the relationship with frailty was modified by sex, age or household income. RESULTS: community-living adults (50-90 years) with greater pre-pandemic frailty reported more negative impacts during the first year of the pandemic. The frailty gradient was not explained by socio-demographic or health behaviour factors. The largest absolute difference in adjusted prevalence between the most and least frail quartiles was 15.1% (challenges accessing healthcare), 13.3% (being ill) and 7.4% (increased verbal/physical conflict). The association between frailty and healthcare access differed by age where the youngest age group tended to experience the most challenges, especially for those categorised as most frail. CONCLUSION: although frailty has been endorsed as a tool to inform estimates of COVID-19 risk, our data suggest it may have a broader role in primary care and public health by identifying people who may benefit from interventions to reduce health and social impacts of COVID-19 and future pandemics.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".