SYMPTOM BURDEN, POSITIVE AFFECT AND RESILIENCY IN OLDER ADULTS EXPERIENCING NORMAL AGING
Bibliographic record
Abstract
Abstract Older adults (age≥65) with multimorbid disease burden may be at chronic risk for bothersome symptoms, which can consequently reduce positive affect (e.g., feelings of excitement and enthusiasm). However, we hypothesized that individuals with more resiliency (the ability to “bounce back” from distress) may experience positive affect despite symptom burden. We conducted secondary analysis of baseline self-report survey data (collected between 8/2023-10/2023) from a prospective study of resiliency in older adults residing in nine continuing care retirement communities (CCRCs) across the U.S. Eligible participants lived independently in CCRCs and scored >11/15 on the five-minute Montreal Cognitive Assessment. Surveys assessed symptom burden (Condensed Memorial Symptom Assessment Scale), resiliency (Current Experience Scale), and positive affect (Positive and Negative Affect Schedule-Positive Affect). Bivariate correlations and multiple linear regression evaluated associations of symptom burden and resiliency with positive affect. Participants (M age =81, SD=5.6) were predominantly White (96%) and female (75%). Greater symptom burden correlated with lower positive affect (r=-.295, p<.001) and lower resiliency (r=-2.56, p<.001). In a multiple regression model, lower symptom burden (β = -.136, p=.003) and more resiliency (β=.626, p<.001) were independently associated with greater positive affect. The overall model accounted for 45.4% of variance in positive affect (F(2, 284) =117.198, p<.001). Lower symptom burden and more resiliency contributed to more positive affect among older adults in senior living communities. Because resiliency and positive affect are both potentially modifiable targets for intervention, longitudinal study and psychosocial interventions should explore the effects of increased resiliency on positive affect and general well-being over time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".