Moving Forward from COVID-19: Exploring Diverse Formats of Analytical Psychodrama and Comparing Clinical Outcomes between Online and In-Person Sessions
Bibliographic record
Abstract
Introduction: Amid the COVID-19 pandemic, the psychological support service of the University of Bologna (SAP, Chief – Professor Silvana Grandi) provided remote psychological therapy to students. Among the interventions implemented, the Analytical Psychodrama via videoconferencing (VAP) has shown promise in reducing psychological issues and enhancing emotional competence in young adults. Following the COVID-19 emergency, the service reinstated In-Person Analytical Psychodrama (IPAP) with a shorter format of 24 sessions instead of 38. This study aims to compare the outcomes of 38-session VAP interventions with 24-session IPAP interventions to understand the impact of analytical psychodrama (AP) format on clinical outcomes. Methods: 36 young adults (M = 25%) with anxiety-depressive problems took part in 38 weekly group sessions of VAP (N = 22) and 24 weekly group sessions of IPAP (N = 14). The Clinical Outcomes in Routine Evaluation – Outcome Measure (CORE-OM), the Trait Emotional Intelligence Questionnaire – Short Form (TEIQue-SF), the Toronto Alexithymia Scale (TAS-20), the Interpersonal Reactivity Index (IRI) and the Group Climate Questionnaire (GCQ) were used to assess clinical outcomes, emotional competence and group climate at baseline (T1) and after treatment (T2). A Repeated Measure ANOVA was conducted to compare T1 and T2 scores, considering the AP format as between-subjects factor. Results: Patients showed significantly different scores between T1 and T2 (p <.001), indicating improvements across all dimensions of the CORE-OM (except for the Risk subscale); in the Well-being and Self-control dimensions, and in the Global Score of the TEIQue-SF; TAS-20 global score; Personal Distress dimension of the IRI; and the Engagement and Conflict dimensions of the GCQ. Despite the distinct settings and durations associated with VAP and IPAP, no significant differences emerged between T1 and T2 based on the format of the AP. Discussion: The findings support the feasibility of implementing AP across different formats (online vs. in-person; brief vs. extended), suggesting the potential for diversified interventions tailored to specific patient profiles and needs, while ensuring effectiveness in clinical practice.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".