Associations between dispositional optimism and diurnal cortisol in a community sample: When stress is perceived as higher than normal.
Bibliographic record
Abstract
OBJECTIVES: This study examined whether dispositional optimism would be associated with reduced levels of cortisol secretion among individuals who perceive stress levels that are either higher than their normal average (i.e., within-person associations) or higher than the stress levels of other individuals (i.e., between-person associations). METHODS: Stress perceptions and four indicators of diurnal cortisol (area-under-the-curve, awakening, afternoon/evening, and cortisol awakening response [CAR] levels) were assessed on 12 different days over 6 years in a sample of 135 community-dwelling older adults. RESULTS: Hierarchical linear models showed that although pessimists secreted relatively elevated area-under-the-curve, awakening, and afternoon/evening levels of cortisol (but not CAR) on days they perceived stress levels that were higher than their normal average, optimists were protected from these stress-related elevations in cortisol. However, when absolute stress levels were compared across participants, there was only a significant effect for predicting CAR (but not the other cortisol measures), indicating that optimism was associated particularly strongly with a reduced CAR among participants who experienced high levels of stress. CONCLUSIONS: Dispositional optimism can buffer the association between stress perceptions and elevated levels of diurnal cortisol when individuals perceive higher-than-normal levels of stress, and it may predict a reduced CAR among individuals who generally perceive high stress levels. Research should examine relative, in addition to absolute, levels of stress to identify the personality factors that help individuals adjust to psychological perceptions of stress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".