Attrition and attendance in group therapy for university students: An examination of predictors across time
Bibliographic record
Abstract
OBJECTIVES: There exists a dearth of research focused explicitly on predictors of attrition, particularly in the area of group therapy, where both attrition and attendance becomes of primary concern. The present study examined both pretreatment and treatment-specific variables in the prospective prediction of attendance and attrition in group therapy. METHOD: Fifty-two participants were randomized to one of two 12-week group treatments. Participants completed baseline interviews and questionnaires, as well as weekly assessments of treatment-specific factors. RESULTS: No pretreatment factors predicted attendance or drop out, although men attended a larger amount of sessions and were less likely to drop out. Cross-lagged panel analyses supported bidirectional, causal relationships both treatment-specific predictors (therapeutic alliance and number of therapeutic techniques) and attendance. CONCLUSIONS: Successful retention in group therapy may be less predictable from pretreatment factors and instead lie in increasing alliance and fostering the practice of therapeutic strategies.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".