The interplay of forgiveness by God and self-forgiveness: a longitudinal study of moderating effects on stress overload in a religious Canadian sample
Bibliographic record
Abstract
BACKGROUND: A consistent link between self-forgiveness and well-being has been established, yet a full understanding of self-forgiveness and its correlates, particularly in relation to forgiveness by God, remains limited, especially given that most existing data are cross-sectional. This study sought to address this gap by investigating the interplay between self-forgiveness and perceived forgiveness by God in reducing stress overload among religious individuals over time. METHODS: This study involved 211 religious individuals in Canada, 55% of whom were female. Through multilevel analyses, the research examined the between-person, within-person, and cross-level effects of these forms of forgiveness on stress across three waves conducted over a total 12-month period. RESULTS: The findings suggested that the effectiveness of self-forgiveness in mitigating stress may be significantly influenced by the perception of forgiveness by God, with the greatest stress reduction occurring when forgiveness by God was perceived at higher levels. CONCLUSIONS: These findings highlight the potential value of incorporating spiritual dimensions into psychological approaches to stress management, offering insights into the complex relationships between different forms of forgiveness and their impact on mental health of religious individuals. Future research is encouraged to further explore these dynamics across diverse cultural and religious contexts.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".