Peer‐counseling for women newly diagnosed with breast cancer: A randomized community/research collaboration trial
Bibliographic record
Abstract
BACKGROUND: We conducted a randomized controlled trial of peer-counseling for newly diagnosed breast cancer (BC) patients as a community/research collaboration testing an intervention developed jointly by a community-based-organization serving women with cancer and university researchers. METHODS: We recruited 104 women newly diagnosed with BC at any disease stage. Prior to randomization, all received a one-time visit with an oncology nurse who offered information and resources. Afterwards, we randomized half to receive a match with a Navigator with whom they could have contact for up to 6 months. We recruited, trained, and supervised 30 peer counselors who became "Navigators." They were at least one-year post-diagnosis with BC. Controls received no further intervention. We tested the effect of intervention on breast-cancer-specific well-being and trauma symptoms as primary outcomes, and several secondary outcomes. In exploratory analyses, we tested whether responding to their diagnosis as a traumatic stressor moderated outcomes. RESULTS: We found that, compared with the control group, receiving a peer-counseling intervention significantly improved breast-cancer-specific well-being (p=0.01, Cohen's d=0.41) and maintained marital adjustment (p=0.01, Cohen's d=0.45) more effectively. Experiencing the diagnosis as a traumatic stressor moderated outcomes: those with a peer counselor in the traumatic stressor group improved significantly more than controls on well-being, trauma and depression symptoms, and cancer self-efficacy. CONCLUSIONS: Having a peer counselor trained and supervised to recognize and work with trauma symptoms can improve well-being and psychosocial morbidity during the first year following diagnosis of BC. Cancer 2016;122:2408-2417. © 2016 American Cancer Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".