The Role of Emotion Regulation in the Vicarious Trauma Risk Reduction among Psychotherapists
Bibliographic record
Abstract
The relevance of the study is determined by the increasing risk of vicarious trauma among psychotherapists because of their high emotional and professional stress. The study explores the link between emotion regulation training and vicarious trauma risk, well-being, and professional functioning in psychotherapists. The study employed the methods of testing and questionnaire survey (Emotion Regulation Questionnaire (ERQ), Perceived Stress Scale (PSS), and Professional Quality of Life Scale (ProQOL)). The following statistical methods were also used: descriptive statistics, Shapiro-Wilk test, paired t-test, Wilcoxon test, effect coefficients, multiple regression analysis, 95% confidence intervals. The reliability of the instruments was tested using Cronbach α. The intervention was associated with improvements in emotion regulation, stress reduction, and vicarious trauma symptoms (all p < 0.001). However, the absence of a control group precludes definitive causal attributions, as changes may reflect external factors (e.g., natural recovery, concurrent supervision). Despite this limitation, effect sizes (Cohen’s d = 0.55–0.70) and 12-month stability suggest clinical promise warranting future RCTs. Changes remained stable over the year, but some indicators showed partial regression. The women demonstrated higher levels of well-being, while gender differences on other parameters were minimal (p = 0.041). Furthermore, findings are contextually bound to urban psychotherapists in Kyiv due to purposive sampling; generalizability to rural settings or distinct healthcare systems requires verification.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".