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Record W4401985286 · doi:10.1159/000540476

Associations between Daily Stressors, Health, and Affective Responses among Older Adults: The Moderating Effect of Age

2024· article· en· W4401985286 on OpenAlexaff
Shira Peleg, Miriam Wallimann, Theresa Pauly

Bibliographic record

VenueGerontology · 2024
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsStressorGerontologyPsychologyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.397
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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