The Effect of Bonyan-Method Experiential Marathon Structured Groups (BEMSG) on the Elements of the Five-Factor Model of Personality
Bibliographic record
Abstract
Various studies have shown the effectiveness of marathon groups on improving participants' personality components. For the first time, the present study has studied the effectiveness of one of the oldest marathon groups in Iran on the personality elements of the Big Five model in the nonclinical population. This study was performed quasi-experimental with an experimental (n = 50) and a control group (n = 50). To meet the entry criteria, all applicants were screened while completing a comprehensive demographic questionnaire. The experimental group participated in the marathon group on three days in a row (for 36 hours) and three weekly follow-up sessions. At the end of the third follow-up session, the NEO FFI questionnaire was simultaneously given to the experimental and control groups. The mean difference statistical tests showed that the score of all personality elements in the experimental group compared to the control group had changed significantly (P <0.05). Among these, the largest effects size are related to "extraversion", "responsibility" and "openness to experience" (d> 0.4), respectively. Individual and group constructive experiential games and intensive and sequential feedback processes in Bonyan-method experiential marathon groups seem to improve the Big 5 personality components in the nonclinical population. Considering the relationship between improving the components of personality and mental health, it can be predicted that important steps can be taken to promote the community's mental health and prevent psychological damage by using these groups.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| 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".