Later life health optimism, pessimism and realism: Psychosocial contributors and health correlates
Bibliographic record
Abstract
Prior research has established positive outcomes of health optimism (appraising one's health as good despite poor objective health (OH)) and negative outcomes of health pessimism (appraising health as poor despite good OH), yet little is known about their contributors. We examined the role of psychosocial factors (life event stress, depression, dispositional optimism, perceived social support) in health realism (appraising health in accordance with OH), optimism and pessimism among 489 older men and women. We then accounted for the psychosocial factors when examining multiple health correlates of health realism, optimism and pessimism. Controlling for age, gender and income, regression results indicate that depression and social support were associated with less health optimism, while dispositional optimism was associated with greater health optimism among those in poor OH. Dispositional optimism was associated with less health pessimism and life event stress was associated with greater pessimism among those in good OH. Beyond the effects of the psychosocial factors, structural equation model results indicate that health optimism was positively associated with healthy behaviours and perceived control over one's health; health pessimism was associated with poorer perceived health care management. Health optimism and pessimism have different psychosocial contributors and health correlates, validating the health congruence approach to later life well-being, health and survival.
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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) | 0.001 | 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".