Forgiveness and the appraisal-coping process in response to relationship conflicts: Implications for depressive symptoms
Bibliographic record
Abstract
The present investigation assessed the mediating role of appraisal-coping processes in the relation between forgiveness and depressive symptoms associated with intimate relationship conflicts. Study 1 assessed the role of forgiveness in the context of a severe relationship stressor, namely women experiencing dating abuse, along with the appraisal-coping responses and depressive symptoms associated with such a stressor. Study 2 evaluated the function served by forgiveness among men and women in response to non-abusive relationship stressors, including the dissolution of the relationship, and also assessed the relations among forgiveness, appraisal-coping processes, and depressive symptoms. Women who encountered dating abuse were less likely to forgive their partners, and this was linked to higher levels of depressive symptoms. The relation between forgiveness and lower depressive symptoms was partially mediated by lower threat appraisals, secondary appraisals of the effectiveness of emotion-focused coping, and the reduced endorsements of this coping strategy (Study 1). Appraisal-coping processes similarly mediated the relation between forgiveness and depressive symptoms among men and women reporting conflict in an ongoing (non-abusive) relationship or a relationship break-up (Study 2). It is suggested that the relation between forgiveness and diminished distress operates primarily by guiding individuals' appraisals of the conflict and by diminishing the reliance on emotion-focused coping.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".