Optimism and depressive symptoms in an adult lifespan sample: evaluating coping in daily life as a mechanism
Bibliographic record
Abstract
Objectives Dispositional optimism is a protective factor for mental health, but the mechanisms behind this relationship are unclear. Previous literature suggests coping may be involved. Optimism may impact how often and how flexibly one employs problem-focused, support-seeking, or cognitive strategies when coping with daily stressors, possibly contributing to mental health differences. We examined whether coping strategy use mediated the relationship between optimism and changes in depressive symptoms.Methods and Measures A community-based sample of 240 adults completed baseline assessments of optimism and depressive symptoms, followed by a 2-week experience sampling period where participants reported daily stressors and stressor-specific coping strategy use 4× daily. Participants then completed another questionnaire measuring depressive symptoms.Results Optimism predicted reductions in depressive symptoms from baseline to follow-up, but this was not mediated by coping strategy frequency or flexibility (p values > .05). Follow-up analyses demonstrated that optimism was related to lower depressive symptoms among younger and middle-aged and not among older adults, but no differences emerged during multiple group mediation analyses. Coping strategy use also had no direct effect on depressive symptoms (p values > .05).Conclusion Our study supports the protective role of optimism on depressive symptoms even over a short-term period, but research on mechanisms such as coping efficacy is warranted.
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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 | 0.001 | 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".