Effectiveness of Positive Psychotherapy on Depression and Alexithymia in Women Applying for a Divorce
Bibliographic record
Abstract
BACKGROUND: The new therapeutic approach of positive psychotherapy has successfully treated severe mental disorders such as depression and mood disorders. However, existing research has not sufficiently measured the usefulness of this treatment in reducing depression and alexithymia. OBJECTIVES: This study thus examined the effectiveness of positive psychotherapy in reducing these two conditions in a specific population: Iranian women applying for the divorce. METHODS: A total of 40 participants aged 20-40 with a high score in the Beck Depression Inventory and Toronto Alexithymia Questionnaire were recruited from women referred to a psychology clinic for divorce-related problems. The pretest, posttest, and follow-up were conducted with all participants, who were randomly placed in two groups: the experimental and control groups, which each consisted of 20 people. We provided eight positive psychotherapy sessions for only the experimental group. RESULTS: After MANCOVA was conducted, the results showed that positive psychotherapy significantly decreased alexithymia and depression in the test population.
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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 | 0.000 | 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".