Breathe with the Waves (BWW)—Creating and Assessing the Potential of a New Stress Management Intervention for Oncology Personnel
Bibliographic record
Abstract
Healthcare providers in oncology experience exceptionally high stress rates. Research emphasizes that stress management programs must be quick to implement, flexible to accommodate demanding schedules, cost-effective, accessible to all staff, and tailored to the needs of oncology personnel. Programs that fail to meet these criteria often struggle with uptake and sustainability. This mixed-methods exploratory study aimed (1) to design an online stress management program, Breathe with the Waves (BWW), based on breathing techniques; (2) to evaluate its acceptability, satisfaction, and relevance; (3) to identify perceived benefits and challenges; and (4) to generate potential outcome measures for future studies. A team of Canadian researchers and end-users co-designed the intervention. Twenty oncology professionals completed BWW, which featured pre-recorded breathing videos, and provided feedback via questionnaires and semi-structured interviews. We used t-tests and Wilcoxon rank tests to analyze quantitative data, and template analysis for qualitative data. Participants found BWW highly acceptable, satisfactory, and relevant. Participants reported three categories of benefits: stress reduction, improved work performance, and increased mindfulness. Challenges included anticipated challenges and experienced challenges. Potential outcome measures fell into six categories: physical health, mental health, relational, work, mindfulness and personal practice. BWW, available in English and French, represents a promising and accessible approach to supporting the well-being of oncology personnel.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".