Illustrating the Multi-Faceted Dimensions of Group Therapy and Support for Cancer Patients
Bibliographic record
Abstract
In cancer support groups, choice of therapy model, leadership style, and format can impact patients' experiences and outcomes. Methodologies that illustrate the complexity of patients' group experiences might aid in choosing group style, or testing therapeutic mechanisms. We used this naturalistic study as a beginning step to explore methods for comparing cancer group contexts by first modifying a group-experience survey to be cancer-specific (Group Experience Questionnaire (GEQ)). Hypothesizing that therapist-led (TL) would differ from non-therapist-led (NTL), we explored the GEQ's multiple dimensions. A total of 292 patients attending three types of groups completed it: 2 TL groups differing in therapy style ((1) Supportive-Expressive (SET); (2) The Wellness Community (TWC/CSC)); (3) a NTL group. Participants rated the importance of "Expressing True Feelings" and "Discussing Sexual Concerns" higher in TL than NTL groups and "Discussing Sexual Concerns" higher in SET than other groups. They rated "Developing a New Attitude" higher in TWC/CSC compared to NTL. In addition, we depict the constellation of group qualities using radar-charts to assist visualization. These charts facilitate a quick look at a therapy model's strengths and weaknesses. Using a measure like the GEQ and this visualization technique could enable health-service decision making about choice of therapy model to offer.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".