Effectiveness of the Luthans Psychological Capital Intervention Model on Resilience and Social Adaptation of Turkmen Divorced Women
Bibliographic record
Abstract
Objective: The aim of the study was to investigate the effectiveness of the Luthans psychological capital intervention model on the resilience and social adaptation of Turkmen divorced women. Method: The research method was a quasi-experimental design with a control group, pre-test, post-test, and two-month follow-up. The population consisted of all Turkmen divorced women who had visited the Behravan counseling centers in Gonbad Kavous city in the first four months of the year 2019. Among them, 30 participants were selected and randomly assigned to either the experimental or control group. The experimental group received weekly group interventions in 10 sessions of 90 minutes each. Both groups completed the Resilience (Connor & Davidson, 2003) and Social Adaptation (Weissman & Paykel, 1974) questionnaires at three stages: pre-test, post-test, and follow-up. Data were analyzed using mixed ANOVA with repeated measures. Findings: The findings showed a significant difference between the two groups in resilience (F=8.79, p=0.006) and social adaptation (F=5.39, p=0.028) at the post-test and follow-up stages. Conclusion: Based on the findings of this study, it can be concluded that training in the psychological capital intervention model helps improve the quality of life of divorced women by enhancing resilience and social adaptation.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".