Valeological aspects of emotional regulation and practices for getting out of Karpman's "Triangle of Suffering"
Bibliographic record
Abstract
Cite in Vancouver style as: Shevchenko AS, Shumskyi OL, Nesterenko VG, Burbyha VA, Kucherenko SM, Kucherenko NS, Shayda VP, Gavrylov EV. Valeological aspects of emotional regulation and practices for getting out of Karpman’s "Triangle of Suffering". Inter Collegas. 2025;12(2):109-21. https://doi.org/10.35339/ic.2025.12.2.ssn Archived: https://doi.org/10.5281/zenodo.17055940 Abstract Background. Karpman’s "Triangle of Suffering" is a model of social interaction of people who are in "toxic", conflict relationships in the roles of mainly the Victim, Persecutor and Rescuer, experience negative emotions (fear, resentment, guilt, anger, aggression) and generate such emotions in other participants in Karpmanian relationships. These negative emotions can cause mental disorders, social maladjustment and psychosomatic pathology; therefore, when teaching valeological disciplines, it is necessary to show how to find a way out from Karpman’s triangle through the self-regulation of emotions. There is a lack of empirical research that proves the success of such training. Aim. Studying the practices of coming out of Karpman’s "Triangle of Suffering" and efficiency of emotional self-regulation in non-medical students when learning valeological disciplines. Materials and Methods. The study was carried out using the method of system analysis, sociological and bibliosemantic methods (97 literary sources were analyzed). The study included a sample of 124 students, equally divided by gender (62 males and 62 females), with an average age of 20.4 years. Participants were divided into control (n=17) and main groups according to the criteria for their participation in the Karpman’s triangle, the chosen strategies for exiting the triangle and the implementation of the exit intention. We proposed two strategies to exit the Karpman triangle, namely defensive (termination of communication with so called "Karpman’s team members") and Emotional-Energy Transformation (EET, reaching a new energy level in a triangle with a change of roles and transformation of emotions). Emotional interaction was assessed using the Difficulties in Emotion Regulation Scale twice with an interval of at least 1 month between surveys. Statistical analysis included descriptive statistics (M±SD), comparative analysis (t-test), correlation studies, and calculation of effect magnitude (Cohen’s d). The study was approved by the ethics committees of two scientific institutions. Results and Conclusions. Among the 124 participants in the study, 24 students chose the EET strategy, of which 16 people fully implemented it. EET produced the best emotional regulation scores (average DERS reduction of [42.5±4.7] points at 87.5%). The defensive strategy chosen by 5 participants (of whom only 1 person implemented) showed an average decrease in DERS of only [19.8±3.2] points. Keywords: strategies for getting out of toxic relationships, Victim, Rescuer, Persecutor, transformation of emotions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".