Testing two digital stress-management interventions in a randomized controlled trial of breast cancer patients
Bibliographic record
Abstract
The digital stress-management intervention StressProffen has been shown to be associated with improved well-being and quality of life for cancer survivors. In the Coping After Breast Cancer (CABC) trial, effects of 6 months' access to modified versions of StressProffen, delivered through a digital download-only model, were examined. Women with breast cancer were invited to participate in the trial 6-9 months following diagnosis. Eligible participants were randomized to either: (1) digital cognitive behavioral therapy stress-management intervention (CBI), n = 140, (2) digital mindfulness-based stress-management intervention (MBI), n = 143, or (3) usual-care (control group), n = 147. Primary outcome was change in perceived stress level (PSS-10), while secondary outcomes included changes in health-related quality of life (HRQoL), anxiety and depression, fatigue, mindfulness, sleep and coping. Perceived stress level at baseline was low for all groups. No statistically significant mean differences (MD) were detected between either of the intervention groups and the control group from baseline to 6-month follow-up for perceived stress level (MBI: MD -0.28 [95%CI: -1.75, 1.19], CBI: MD -0.42 [95%CI: -1.89, 1.06]), nor for the majority of the secondary outcomes. After 6 months of access, the CBI and MBI stress-management interventions did not yield significantly improved outcomes for women with breast cancer compared with usual-care controls. Further explorations of which interventions and delivery models may optimize use and effect, best timing for delivery, and individual preferences are needed. ClinicalTrials.gov identifier NCT04480203.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".