Health incongruence in later life: Implications for subsequent well-being and health care.
Bibliographic record
Abstract
OBJECTIVE: The premise that pessimistic health appraisals compromise well-being whereas optimistic appraisals are compensatory was examined in a longitudinal study of 232 community-dwelling older adults (ages 79-98 years). DESIGN: Subjective health (SH) appraisals were contrasted with objective health (OH) to identify realists, whose ratings were congruent (SH = OH), distinguishing them from health pessimists (SH < OH) and health optimists (SH > OH), whose ratings were incongruent. Analyses of covariance were used to examine group differences 2 years later on well-being and health care. MAIN OUTCOME MEASURES: Outcome measures were psychological well-being (life satisfaction, positive and negative emotions), functional well-being (objective and perceived physical activity, activity restriction), and health care (health care management, hospital admissions, length of hospital stays). RESULTS: Compared with realists, pessimists had significantly poorer outcomes and optimists had better outcomes. Because perceived control (PC) was weaker among pessimists and stronger among optimists, supplemental analysis determined whether PC differences explained these findings. When accounting for PC, many pessimism and optimism effects became nonsignificant, yet effects on functional well-being remained unchanged. CONCLUSION: Findings have implications for older adults at risk of functional decline.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".