Co-creating a yoga program for women diagnosed with gynecologic cancer: a consensus study
Bibliographic record
Abstract
PURPOSE: Yoga may be uniquely suited to address bio-psycho-social concerns among adults with gynecologic cancer because it can be tailored to individuals' needs and can help shift focus inward towards self-reflection, body appreciation, and gratitude. This study describes the collaborative process guided by the Knowledge-to-Action framework used to develop a yoga program for adults diagnosed with gynecologic cancer and inform a feasibility trial. METHODS: In 3 collaborative phases, yoga instructors and women diagnosed with gynecologic cancer formulated recommendations for a yoga program and evaluated the co-created program. RESULTS: The program proposed is 12 weeks in length and offers two 60-min group-based Hatha yoga classes/week to five to seven participants/class, online or in person, with optional supplemental features. Overall, participants deemed the co-created program and instructor guidebook to be reflective of their needs and preferences, though they provided feedback to refine the compatibility, performability, accessibility, risk precautions, and value of the program as well as the instructor guidebook. CONCLUSION: The feasibility, acceptability, and benefits of the program are being assessed in an ongoing feasibility trial. If deemed feasible and acceptable, and the potential for enhancing patient-reported outcomes is observed, further investigation will focus on larger-scale trials to determine its value for broader implementation.
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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 | 0.036 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".