Loneliness and resilience are associated with nutrition risk after the first wave of COVID-19 in community-dwelling older Canadians
Bibliographic record
Abstract
Nutrition risk is linked to hospitalization, frailty, depression, and death. Loneliness during the coronavirus disease 2019 (COVID-19) pandemic may have heightened nutrition risk. We sought to determine prevalence of high nutrition risk and whether loneliness, mental health, and assistance with meal preparation/delivery were associated with risk in community-dwelling older adults (65+ years) after the first wave of COVID-19 in association analyses and when adjusting for meaningful covariates. Data were collected from 12 May 2020 to 19 August 2020. Descriptive statistics, association analyses, and linear regression analyses were conducted. For our total sample of 272 participants (78 ± 7.3 years old, 70% female), the median Seniors in the Community: Risk evaluation for Eating and Nutrition (SCREEN-8) score (nutrition risk) was 35 [1st quartile, 3rd quartile: 29, 40], and 64% were at high risk (SCREEN-8 < 38). Fifteen percent felt lonely two or more days a week. Loneliness and meal assistance were associated with high nutrition risk in association analyses. In multivariable analyses adjusting for other lifestyle factors, loneliness was negatively associated with SCREEN-8 scores (-2.92, 95% confidence interval [-5.51, -0.34]), as was smoking (-3.63, [-7.07, -0.19]). Higher SCREEN-8 scores were associated with higher education (2.71, [0.76, 4.66]), living with others (3.17, [1.35, 4.99]), higher self-reported health (0.11, [0.05, 0.16]), and resilience (1.28, [0.04, 2.52]). Loneliness, but not mental health and meal assistance, was associated with nutrition risk in older adults after the first wave of COVID-19. Future research should consider longitudinal associations among loneliness, resilience, and nutrition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".