The Effectiveness of Compassion-Based Spouse-Treating Education on the Improvement of Family Relationships and Forgiveness in Married Deaf and Hard of Hearing Men in Isfahan
Bibliographic record
Abstract
The present study aimed to evaluate the effectiveness of compassion-based spouse-treating education on improving family relationships and forgiveness among married deaf and hard of hearing men in Isfahan. This research was a quasi-experimental study with a pre-test, post-test, and follow-up design, or control group. The sample consisted of 30 volunteer men from the deaf or hard of hearing population in Isfahan, who were non-randomly (conveniently) assigned to either the experimental or control groups, with the control group being on a waiting list. The experimental group received an educational package over 8 sessions of 120 minutes each. The research tools were the Olson and Barnes Family Relationships Questionnaire (2004) and the Thompson et al. Forgiveness Questionnaire (2005). Both groups were assessed at three stages: pre-test, post-test, and follow-up. Descriptive and inferential statistics (repeated measures analysis of variance) were used for data analysis (p < 0.01). The results showed that compassion-based spouse-treating education significantly improved family relationships and forgiveness. According to the findings of this research, it can be said that compassion-based spouse-treating education is an appropriate method for improving marital relationships and increasing forgiveness among deaf men.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".