Association of loneliness, social isolation, and daily cognitive function in Mexican older adults living in community during the first wave of COVID-19 pandemic
Bibliographic record
Abstract
Introduction. Loneliness and social isolation are known risk factors for cognitive decline; their effect in older adults (OA) after COVID-19 lockdown is emerging. Objective. To establish an association between loneliness and social isolation, with daily cognitive function in Mexican OA during the first wave of the COVID-19 pandemic. Method. Cross-sectional study, derived from the cohort “The impact of COVID 19 on well-being, cognition, and discrimination among older adults in the United States and Latin America”, which included 308 OA recruited between March-August 2020 whose daily cognitive function were determined with the Everyday Cognition Scale (E-Cog) as dichotomized score (cut point: 1.31 for normal cognition). Loneliness and social isolation were binomial variables. Results. The mean age was 65.4 ± 7.9 years, 75.7% were women. The mean continuous E-Cog score was 57.4 (SD = ± 19.1), 49.1% had a score () 1.31 (normal cognition), while 50.9% had a higher score (cognitive impairment). Eighty four percent of participants reported loneliness, 79.9% reported social isolation. Multivariate regression model showed a negative and statistically significant association between social isolation and loneliness and E-Cog, adjusted by age, sex and education level (β = -.046, 95% CI = [-.8, -.013], p = .007; β = -.16, 95% CI = [-.08, -.018], p = .003), and a positive association with subjective memory complaint (β = .81, 95% CI = [-.16, -.11], p = () .001). Discussion and conclusion. These data suggest the need for increased vigilance of those who have loneliness and social isolation due to its potential deleterious effect on cognitive function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".