EMOTION REGULATION AND DEPRESSION IN SENIORS: THE ROLE OF EXECUTIVE FUNCTIONS AND PERCEIVED STRESS
Bibliographic record
Abstract
Depression is a mental health disorder that significantly impacts quality of life in older adults. Chronic perceived stress and emotion regulation (ER) are both associated with onset and maintenance of depression. Maladaptive ER has been linked to greater perceived stress levels and prolonged experience of depressed mood. It is suggested that the use of adaptive ER strategies requires different domains of executive functions (EF), which commonly decline with chronic stress and increase in age. Given that some ER strategies require greater cognitive resources, it is plausible that both perceived stress and EF may modify the relationship between ER and depression. The present study examined the role of EF and perceived stress in the relationship between ER and depression among community-dwelling older adults. Participants (N = 70) aged 60+ completed the Trail-Making Task B, the Cognitive Emotion Regulation Questionnaire, the Beck Depression Inventory-II, and Perceived Stress Scale-10 as part of a larger study. Controlling for age, sex, education, and perceived socioeconomic status, a significant indirect effect of maladaptive ER on depression through perceived stress was found (0.47, 95% CI [0.21, 0.84]). This mediation was moderated by levels of EF (Maladaptive ER x EF: β = .01, t = 2.20, p = .03). This study is the first to elucidate the interrelationship between underlying mechanisms of depression and may help support the development of personalized programs that help maintain emotional well-being among older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".