Risk Predictors and Cognitive Outcomes of the Psychosocial Functioning of North American Older Adults During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic caused a global mental health deterioration. The disruption of older adults’ psychosocial functions is particularly concerning given their social support and technology use barriers. Despite a close relationship between social engagement and cognitive function in older adults, little is known about the cognitive consequences of older adults’ disrupted psychosocial functions in the context of the pandemic. Aims: This study aims to identify sociodemographic and COVID-19-related predictors for psychosocial functioning in North American older adults and to examine their associated cognitive outcomes. Methods: A sample of 95 older adults aged 60 and older (M = 68.85, SD = 6.458) completed an online study from January to July 2021, including a questionnaire on sociodemographic and COVID-19-related experiences, the Kessler-10 (K10) to assess psychological distress, Satisfaction with Life Scale (SWLS) and the UCLA Loneliness Scale Revised (UCLA) to index social function, and the Go/No-go Task (GNG) and Letter Comparison Task (LCT) as cognitive measures. Results: Higher psychosocial functioning was predicted by increased approach-based coping, being aged 65–69, 70–74, and over 75 years relative to being 60–64, and being in medium to excellent relative to poor health, while lower psychosocial functioning was predicted by increased avoidance based coping strategies and having average relative to low income. Psychosocial functioning was not seen to strongly predict cognitive functioning. However, being aged 75 years and older relative to being aged 60–64 predicted decreased accuracy on no-go trials and slower cognitive speed, and lower LCT accuracy was predicted by more avoidance-based coping and being in a religion other than Christianity or Catholicism (e.g., being spiritual). Conclusions: The results identified age, income, and health status as psychosocial function predictors among North American older adults, and increased age, religion, and use of avoidance-based coping strategies as predictors for decreased cognitive performance. The results shed light on future public health strategies to promote the psychosocial and cognitive health of older adults.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".