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Record W4405961376 · doi:10.1080/13607863.2024.2445136

Changes in optimism and subsequent health and wellbeing outcomes in older adults: an outcome-wide analysis

2025· article· en· W4405961376 on OpenAlexafffund
Ying‐Yeh Chen, Julia S. Nakamura, Eric S. Kim, Laura D. Kubzansky, Tyler J. VanderWeele

Bibliographic record

VenueAging & Mental Health · 2025
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsUniversity of British Columbia
FundersMichael Smith Health Research BCJohn Templeton Foundation
KeywordsOptimismGerontologyPsychologySuccessful agingClinical psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: This study examined whether changes in optimism in older adulthood are associated with subsequent health and wellbeing outcomes. METHOD: = 12,998, 2006/2008 to 2014/2016 waves). To evaluate changes in optimism, we examined optimism assessed in 2010/2012 and adjusted for optimism assessed 4 years earlier in 2006/2008 in regression models, which, under the specified statistical models, is equivalent to assessing changes in optimism during the 4-year interval. We examined 35 outcomes assessed in 2014/2016, including: indicators of physical health, health behaviors, psychological distress, psychological wellbeing, and social factors. RESULTS: Increases in optimism (e.g. from the lowest to highest quartile) were favorably associated with several physical health outcomes such as a reduced risk of mortality (relative risk [RR] = 0.76; 95% confidence interval [CI]: 0.62, 0.94) and better self-rated health, but were not associated with specific disease outcomes (e.g. diabetes, stroke) or health behaviors. Increased optimism was also inversely associated with all psychological distress indicators and positively associated with multiple aspects of psychosocial wellbeing. CONCLUSION: An optimistic mindset may be desirable in its own right. Increased optimism may also enhance health and wellbeing among older adults.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.358
Teacher spread0.342 · 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.

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

Citations3
Published2025
Admission routes2
Has abstractyes

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