PERCEIVED CONTROL AND CORTISOL STRESS REACTIVITY: VARIATIONS BY AGE, RACE, AND FACETS OF CONTROL
Bibliographic record
Abstract
Abstract Greater perceived control is associated with better aging-related health outcomes, and these associations have previously been shown to differ based on sociodemographics. Physiological stress responses—including cortisol reactivity to stressors—may underlie the link between perceived control and health. The goal of this study was to evaluate the associations of perceived control and its facets (personal mastery and perceived constraints) with cortisol reactivity to acute laboratory stressors, in addition to the moderating roles of age and race. Participants (N = 737) ages 25-75 completed a perceived control questionnaire and two lab-based stress tasks. Salivary cortisol was collected pre- and post-stressor exposure. The results showed no main effects of perceived control, personal mastery, nor perceived constraints on salivary cortisol reactivity to stressors. However, age and race moderated the association between perceived constraints and post-stressor cortisol level, adjusting for baseline cortisol, sociodemographics, and health covariates. Among white participants, younger adults who reported higher constraints had elevated cortisol responses compared to those who reported lower constraints, whereas constraints were unrelated to cortisol reactivity among midlife and older adults. Among black participants, perceived control and its subscales were unrelated to cortisol, regardless of age. These findings suggest that older age buffers against the association between constraints and stress reactivity, but this buffering effect is only evident for white participants. Future research on the role of perceived control in stress and health should consider the importance of racial differences, facets of control, and age variations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".