Bibliographic record
Abstract
Many people are consumed with anger, depression, and/or anxiety as a result of harboring feelings of resentment towards an individual who has offended them. Refusing to forgive holds deleterious mental health consequences for the victim; however, forgiveness yields reductions in anger, depression, and anxiety. But, little is known regarding how forgiveness works to improve mental health. Rumination, repetitive and intrusive thoughts about the offense or emotions elicited by the offense, may exacerbate and maintain the negative emotions educed by the offense. Consequently, this study evaluated rumination as a mediator between forgiveness and mental health, hypothesizing that forgiveness works to improve mental health through first eliminating rumination. The results of this study support the conclusion that one mechanism through which forgiveness works to improve mental health is through first reducing rumination. Specifically, interpersonal rumination, ruminating on future interpersonal failure, was found to mediate the relationship between forgiveness and anger, depression, and anxiety. However, forgiveness was also found to hold a direct effect on mental health. Forgiveness predicts decreases in anger, depression, and anxiety over and above the effect of interpersonal rumination.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .S39. Source: Masters Abstracts International, Volume: 44-03, page: 1517. Thesis (M.A.)--University of Windsor (Canada), 2005.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".