اثربخشی رواندرمانی پویشی کوتاهمدت فشرده (ISTDP) بر کاهش نارسایی هیجانی زنان متقاضی طلاق
Bibliographic record
Abstract
The present study was conducted to investigate the effectiveness of intensive short-term dynamic psychotherapy to reducing alexithymia in women applicant for divorce. The research design was based on a semi-experimental with pre-test-post-test approach and control group. The statistical population consisted of all women applicant for divorce, who referred to kermanshah city’s counseling centers in 2017. Among them, 30 people selected in the form of voluntary purposive method and randomly divided into two equal groups: an experimental group and a control group. The intervention of intensive short-term dynamic psychotherapy in the experimental group for 9 sessions of 60 minutes, the control group was put in the waiting list. Toronto Alexithymia Scale-20 (TAS-20) were used in the pretest and posttest for collecting information. Data were analyzed using one-way analysis of covariance. The results of the research showed that after controlling the effect of pre-test, there was a significant difference between the two groups in the post-test stage alexithymia. According to research results, group therapy with short-term dynamic psychotherapy seems to be effective in reducing alexithymia in women applicant for divorce. Therefore, the practice of such a treatment in divorce prevention counseling centers and social worker clinics seems necessary.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".