Perceived control and reactivity to acute stressors: Variations by age, race and facets of control
Bibliographic record
Abstract
Abstract Greater perceived control is associated with better health and well‐being outcomes, possibly through more adaptive stress processes. Yet little research has examined whether facets of perceived control (personal mastery and perceived constraints) predict psychological and physiological stress reactivity. The present study evaluated the associations of personal mastery and perceived constraints with changes in subjective stress and cortisol in response to acute laboratory stressors, with age and race as potential moderators. In the Midlife in the United States Refresher Study (N = 633 adults aged 25–75), participants completed a baseline perceived control measure and were subsequently recruited to participate in the laboratory stress protocol. The protocol consisted of completing two mental stress tasks (mental arithmetic and Stroop) as well as providing saliva samples and subjective stress ratings. Race moderated the association between perceived constraints and subjective stress reactivity, such that higher constraints predicted greater subjective stress responses in White participants, but no association was observed in Black participants. Higher personal mastery and perceived constraints each predicted greater increases in cortisol in response to the stress tasks (AUCi) among younger but not older adults. These findings suggest that older adults were buffered against the association between facets of control and cortisol stress reactivity. Discussion on potential racial differences in the link between constraints and stress reactivity are elaborated further, as well as considerations for future work to distinguish between facets of control and examine age and racial differences.
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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".