Using a Positive Self-Talk Intervention to Enhance Coping Skills in Breast Cancer Survivors: Lessons from a Community-Based Group Delivery Model
Bibliographic record
Abstract
PURPOSE: Cancer survivorship is a distinct phase of the cancer continuum, and it can have myriad associated stresses and challenges. The purpose of the present study was to evaluate the effectiveness of a positive self-talk (pst) intervention in enhancing the coping skills and improving the psychological well-being of breast cancer survivors. METHODS: Participants (n = 38) were recruited from 5 support groups in a small eastern Canadian province. Support groups were randomly assigned to either a control (n = 18) or an intervention (n = 20) condition. Intervention participants were pre-tested, received a 2-hour pst in-person group workshop and a 10-minute "booster" session by telephone, and completed post-test questionnaires 1 month later. RESULTS: Intervention participants reviewed the workshop favourably. Nearly all participants used the intervention in everyday life, were able to accurately describe how pst works, and found that pst had a considerable impact on their ability to cope with cancer and related sequelae. However, the descriptive findings from the workshop evaluation did not translate into significant differences between the intervention and control groups on the psychometric measures. CONCLUSIONS: The pst intervention, delivered in a community group model, was positively received and effective in teaching participants about pst and how pst can be used to enhance coping skills for breast cancer patients. However, the intervention did not promote significantly greater levels of change in anxiety, depression, mood disturbance, or coping ability for intervention participants. The unique challenges of community-level psychological intervention are explored.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".