Changes in optimism and subsequent health and wellbeing outcomes in older adults: an outcome-wide analysis
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".