Advancing complex explanatory conceptualizations of daily negative and positive affect: Trigger and maintenance coping action patterns.
Bibliographic record
Abstract
The present study addressed a fundamental gap between research and clinical work by advancing complex explanatory conceptualizations of coping action patterns that trigger and maintain daily negative affect and (low) positive affect. One hundred ninety-six community adults completed measures of perfectionism, and then 6 months later completed questionnaires at the end of the day for 14 consecutive days to provide simultaneous assessments of appraisals, coping, and affect across different stressful situations in everyday life. Multilevel structural equation modeling (MSEM) supported complex explanatory conceptualizations that demonstrated (a) disengagement trigger patterns consisting of several distinct appraisals (e.g., event stress) and coping strategies (e.g., avoidant coping) that commonly operate together across many different stressors when the typical individual experiences daily increases in negative affect and drops in positive affect; and (b) disengagement maintenance patterns composed of different appraisal and coping maintenance factors that, in combination, can explain why individuals with higher levels of self-critical perfectionism have persistent daily negative affect and low positive mood 6 months later. In parallel, engagement patterns (triggers and maintenance) composed of distinct appraisals (e.g., perceived social support) and coping strategies (e.g., problem-focused coping) were linked to compensatory experiences of daily positive affect. These findings demonstrate the promise of using daily diary methodologies and MSEM to promote a shared understanding between therapists and clients of trigger and maintenance coping action patterns that explain what precipitates and perpetuates clients' difficulties, which, in turn, can help achieve the 2 overarching therapy goals of reducing clients' distress and bolstering resilience.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".