QUALITY OF LIFE AND ATTITUDES TOWARD AGING IN OLDER ADULTS DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Abstract Research has shown that positive or negative views of aging are associated with quality of life. Prior research has found that community dwelling older adults aged 65 and older with more negative views of aging have lower scores on quality of life scales, whereas those with higher views of aging have higher scores on quality of life scales. A group of 264 community dwelling older adults (Mean age = 72.4 years, 62-92 years old) living in Prince Edward Island, Canada, completed a survey measuring attitudes towards aging and quality of life during the COVID-19 pandemic. The sample consisted of a majority of retired (n=206) older adults, living in an urban area (n=151), and approximately 55% receiving a household income of $26,000 to $75,000 per year. Regression analysis found that attitudes towards aging significantly predicted quality of life (F(1,127)=24.9, p< 0.01), with positive attitudes predicting higher quality of life scores and negative attitudes predicting lower quality of life scores. The model showed that the predictor, attitudes towards aging, explained 16.5% of the variance in quality of life (B=-2.7, t=-4.9, p< 0.01). This suggests that attitudes towards aging play a role in predicting quality of life in older adults during the COVID-19 pandemic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".