Effect of Group Therapy for Breast Cancer on Healthcare Utilization
Bibliographic record
Abstract
Purpose: The purpose of this study was to determine whether participation in a group psychosocial intervention by patients with breast cancer would result in an improvement in psychological measures and in reduced billings in general medical expenses. description of Study: Eligible women who had completed treatment for stage 0, I, or II primary breast cancer were prospectively and randomly assigned to either the intervention (n = 46) or control (n = 43) group. Both groups received the usual psychosocial care; however, the intervention group also participated in six weekly cognitive/behavioral psychosocial meetings. All were assessed on psychiatric symptoms, mood, depression, and coping strategies at four time periods: preintervention, postintervention, 1‐year follow‐up, and 2‐year follow‐up. Alberta Healthcare billing records were obtained covering the 2‐year follow‐up period to determine the amount billed per person over the course of the study. Results: Women in the intervention group had less depression, less overall mood disturbance, better overall quality of life, and fewer psychiatric symptoms than those in the control group, beginning immediately postintervention and remaining so at 2 years postintervention. Billing in the intervention group was an average of $147 less than in the control group, a 23.5% reduction. clinical Implications: This is the first study to show that a psychosocial intervention can reduce direct healthcare billings in a sample of patients with cancer. Importantly, these findings help to justify the routine availability of such programs in cancer treatment facilities worldwide.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".