Situational and dispositional coping: an examination of their relation to personality, cognitive appraisals, and psychological distress
Bibliographic record
Abstract
Reliable data on the relationships between situational and dispositional coping strategies are sparse. In order to address this gap in the literature, this study examined the determinants and adaptational outcomes of both types of coping. Two hundred and thirty‐three students completed, along with measures of situational and dispositional coping, measures of personality, cognitive appraisals, and psychological distress, the latter variable being evaluated concurrently and prospectively (10 weeks). Results showed that personality shared as much variance with situational as with dispositional coping, but the patterns of relationships were rather different. In addition, cognitive appraisals were found to add significant incremental validity in predicting situational coping beyond trait coping, but primary appraisals were redundant with personality traits, in particular neuroticism. Finally, in spite of the significant amount of variance shared between the two types of coping, they both accounted for individual differences in concomitant and prospective psychological distress, and the relation between dispositional coping and distress was partially mediated by situational coping. The implications of these findings for understanding the relationships between the two types of coping strategy are discussed. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".