The Relationship between Dimensions of Forgiveness with Mental Health in Mothers of Children with Intellectual and Developmental Disabilities
Bibliographic record
Abstract
The aim of the current study was to predict mental health of the mothers of children with intellectual and developmental disabilities from the magnitude of their forgiveness. To fulfill the stated goal 88 mothers of children with intellectual and developmental disabilities by means of accessible sampling procedure, and Besharat mental health (2009) as well as Enright forgiveness inventories standardized by Ghobari Bonab et al. (2003) was given to them. Analysis of data using Pearson’s correlation revealed that among mental health (psychological well-being and psychological distress) and all three dimensions of forgiveness a positive relation was found. In other words, individuals who were higher in forgiveness were more satisfied in their mental health. Multivariate regression also revealed that 23% of variations in psychological well-being by affective and cognation, and 20% of variations in psychological distress by cognation and behavioral component can be accounted by dimensions of their forgiveness. Theoretical implication and practical application of the findings have been delineated in the original paper.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".