Physical health problems, depressive mood, and cortisol secretion in old age: Buffer effects of health engagement control strategies.
Bibliographic record
Abstract
OBJECTIVE: This study examined the protective role played by control behaviors aimed at overcoming physical health problems (health engagement control strategies; HECS) in the associations between older adults' physical health problems, depressive mood, and diurnal cortisol secretion. It was expected that adaptive levels of HECS would buffer the adverse effects of physical health problems on depressive mood and diurnal cortisol secretion. DESIGN AND MEASURES: Physical health problems and HECS were measured in a cross-sectional sample of 215 community-dwelling older adults. In addition, participants' depressive mood and patterns of diurnal cortisol secretion were assessed across 3 days. RESULTS: The findings demonstrate that physical health problems predicted high levels of depressive mood and diurnal cortisol secretion, but only among older adults who reported low levels of HECS (and not among older adults who reported high levels of HECS). Moreover, depressive mood completely mediated the buffering effect of HECS on the association between physical health problems and cortisol secretion. CONCLUSION: The results suggest that adaptive levels of HECS represent a psychological mechanism that can protect older adults from experiencing the adverse emotional and biological consequences of physical health problems.
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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".