Combination of Transactional Analysis Therapy and Hypnotherapy in the Treatment of Emotional Conflicts: A Case Study in Iran
Bibliographic record
Abstract
The purpose of this study is to introduce a successful combination of transactional analysis therapy and hypnotherapy in the treatment of clients with emotional conflicts. The client was a 38-year-old woman who had visited a clinic due to family conflicts with her husband. Following the first stage of therapy, the family conflicts were resolved by problem focus therapy, so the client stopped the therapy. Yet she revisited the psychological clinic after three months. In the second six sessions, initially Transactional Analysis was used to solve the emotional conflicts. At the end of the sixth session, though, the therapist realized that some of the conflicts had remained unresolved. Therefore, the therapist decided to recreate the principles of transnational analysis indirectly through hypnotic trance and used this synthetic approach to act out emotionally and resolved the conflicts. In the follow-up sessions after the hypnotherapy, the client appeared stable and the therapist witnessed no disturbance in the client’s behaviors and emotions. The client’s emotional conflicts had been resolved.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".