Predicting Change in Emotional Distress from Language Characteristics of Group Psychodynamic Therapy for Perfectionism: An Empirical Case Study
Bibliographic record
Abstract
Though psychodynamic group psychotherapy, like all therapy approaches, espouses the use of specific interventions and distinct mechanisms of change, in general, it is unclear the extent to which different therapy orientations actually differ in practice. The goal of this study was to use Linguistic Inquiry and Word Count (LIWC), a software for quantitative text analysis that counts words and calculates proportions of words from specific predefined categories, as a method for assessing in-session core characteristics of group psychodynamic psychotherapy for perfectionism. LIWC was used to assess the presence of the seven core characteristics found to be unique to individual psychodynamic psychotherapy in the group therapy context, and whether these core psychodynamic group characteristics, when assessed on the word level, predict week-to-week changes in group member-rated perfectionism-related emotional distress. Results suggest that group members' emotional distress increased in early sessions before decreasing in later sessions. Further, core psychodynamic in-session characteristics, including focusing on affect and emotions; identifying patterns in group members' actions, thoughts, feelings, experiences, and relationships; focusing on group members' interpersonal relationships; and focusing on group member-member or leader relationships, all predict less perfectionism-related emotional distress the following week.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".