Associations between Daily Stressors, Health, and Affective Responses among Older Adults: The Moderating Effect of Age
Bibliographic record
Abstract
INTRODUCTION: Reactivity to daily stressors may change as a function of stressor type and age. However, prior research often excludes older adults or compares them to younger age groups (e.g., younger and middle-aged adults). Recognizing older adults as a heterogeneous population with shifting motivations, this study focused on individuals aged ≥65 years and tested age differences in associations between different types of daily stressors, affect, and physical symptoms. METHODS: A total of 108 older adults aged 65-92 years (M = 73.11, SD = 5.92; 58% women) completed daily dairy questionnaires on daily stressors, positive and negative affect, and physical symptoms for 14 consecutive days. Multilevel models were employed, adjusting for sex, age, education, living situation, and day-in-study. RESULTS: Findings revealed age-dependent variations in the associations between daily stressors and affect and physical symptoms. Specifically, external stressors (e.g., finance and traffic stressors) and health stressors were more strongly associated with daily affective states and with overall physical symptoms (respectively) among older age adults. Age did not moderate associations between social stressors and affect or physical symptoms. CONCLUSION: These findings underscore the heterogeneous nature of older adults' responses to daily stressors based on stressor type and age. Specifically, the oldest-old might benefit from personalized support for dealing with challenges such as health and financial stressors.
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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 | 0.000 | 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 | 0.000 |
| 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".