Supportive–expressive group therapy for primary breast cancer patients: a randomized prospective multicenter trial
Bibliographic record
Abstract
OBJECTIVE: The aim is to evaluate the effectiveness of a manualized 12-week supportive-expressive group therapy program among primary breast cancer patients treated in community settings, to determine whether highly distressed patients were most likely to benefit and whether therapist's training or experience was related to outcome. METHOD: Three hundred and fifty-three women within one year of diagnosis with primary breast cancer were randomly assigned to receive supportive-expressive group therapy or to an education control condition. Participants were recruited from two academic centers and nine oncology practices, which were members of NCI's Community Clinical Oncology Program (CCOP) and were followed over 2 years. RESULTS: A 2x2x19 analysis of variance was conducted with main effects of treatment condition, cohort, and baseline distress and their interactions. There was no main effect for treatment condition after removing one subject with an extreme score. Highly distressed women did not derive a greater benefit from treatment. Therapist training and psychotherapy experience were not associated with a treatment effect. CONCLUSIONS: This study provides no evidence of reduction in distress as the result of a brief supportive-expressive intervention for women with primary breast cancer. Future studies might productively focus on women with higher initial levels of distress.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".