The Effect of Emotionally Focused Couple Therapy on Marital Adjustment of Couples Who Came to Consultancy Centers in Kerman City
Bibliographic record
Abstract
Current research was done aiming assessment of effect of emotionally focused couple therapy on marital adjustment of couples. Current research was a semi experimental one. Assessed statistical society were couples who came to consultancy centers in Kerman city. A sample including 40 person or 20 couples (20 women and 20 men) were selected randomly and were placed in two control and experiment groups. Experiment group had received required training within 10 session with 60 minutes each one and control group had not received any training. Data gathering tool was Spanier marital adjustment (2007). After conducting of Pretest on both groups, intervention group were treated by emotionally focused treatment within 10 session. Then Posttest was conducted on both groups. Data analysis was done by Covariance analysis method. MANCOVA analysis results showed that effect of emotionally focused couple therapy on marital adjustment was meaningful. Moreover Covariance analysis on each factor of marital adjustment was also an indication of effect of emotionally focused couple therapy on adjustment factors and satisfaction and on consistency factor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".