Evaluating the Emotion Regulation Program on Enhancing Family Resilience
Bibliographic record
Abstract
The study aimed to explore the effectiveness of emotion regulation program in enhancing family resilience. By comparing the outcomes between experimental and control groups, the research sought to determine the intervention's impact on improving the resilience levels within family units. A total of 40 participants were divided equally into experimental and control groups. The experimental group underwent a emotion regulation program designed to enhance family resilience, while the control group did not receive any intervention. Family resilience was measured for both groups at three time points: pre-test, post-test, and follow-up, using standardized resilience assessment tools. The study employed descriptive statistics, analysis of variance with repeated measurements, and the Bonferroni post-hoc test to analyze the data. The findings indicated significant improvements in family resilience scores for the experimental group from pre-test to post-test, which were largely maintained at follow-up. The analysis of variance revealed significant time, group, and time × group interaction effects, underscoring the intervention's effectiveness. The Bonferroni post-hoc test further confirmed the sustainability of these improvements over time. The study concludes that the emotion regulation program was effective in enhancing family resilience among the participants in the experimental group, with these improvements being sustained over time. These results suggest the potential utility of such interventions in fostering resilience within family units, highlighting the importance of targeted psychological support in resilience enhancement programs.
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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 | 0.002 |
| 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 | 0.000 |
| 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".