Getting back on track: evaluation of a brief group psychoeducation intervention for women completing primary treatment for breast cancer
Bibliographic record
Abstract
OBJECTIVE: Patients with breast cancer experience unmet informational and psychosocial needs at the end of treatment. A brief psychoeducational intervention delivered at this transition may help to address some of the challenges these women face. The purpose of this study was to test the effectiveness of a single-session group psychoeducational intervention (GBOT group) compared with standard print material (usual care). METHODS: In this randomized controlled trial, 442 patients with breast cancer who were completing their adjuvant radiotherapy were recruited and randomized to receive either usual care, which includes standard print material (CRL group n = 226) or usual care and the GBOT group intervention (INT group n = 216). Participants completed measures at baseline and again at 3 and 6 months post-intervention. RESULTS: The INT group showed significant improvement in their knowledge regarding the re-entry transition period (d = 0.31) and in their feelings of preparedness for re-entry (d = 0.37). There were no differences between the groups over time on health-related distress or mood. CONCLUSIONS: Results support the effectiveness of providing a single-session group psychoeducational intervention as a first-step approach to supportive care for women at the end of breast cancer treatment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".