When it is not a good fit: Clinical errors in patient selection and group composition in group psychotherapy.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Group psychotherapy provides unique opportunities for clinical errors in the selection of patients and composition of therapy groups. This article introduces some of the difficulties and complexities that can be associated with group composition and patient selection errors. Clinical vignettes from psychodynamic/interpersonal psychotherapy groups are used to illustrate three variations of group composition and selection errors. The first vignette depicts an error in selecting a disruptive patient into a fledgling group. The second vignette portrays an unsuccessful integration of a withdrawn, inhibited patient into an active, exploratory group. The third scenario illustrates challenges associated with poor quality of object relations in homogeneous group composition. Although research on group therapy composition and patient selection is limited, relevant empirical literature is integrated in our discussion of clinical implications and recommendations. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it