Evaluation of a Virtual Dance Class for Cancer Patients and Their Partners during the Corona Pandemic—A Real-World Observational Study
Bibliographic record
Abstract
BACKGROUND: During the corona pandemic, all courses on physical activity for cancer patients were canceled. The aim of our study was to evaluate the feasibility of switching dancing classes for patients and their partners to online classes. METHODS: Patients and partners from courses at four different locations who consented to the online course offer were asked to fill in a pseudonymous questionnaire on access to the training, technical challenges, acceptance and well-being (1-item visual analog scale from 1 to 10) before and after the training. RESULTS: Sixty-five participants returned the questionnaire (39 patients and 23 partners). Fifty-eight (89.2%) had danced before, and forty-eight (73.8%) had visited at least one course of ballroom dancing for cancer patients before. The first access to the online platform was difficult for 39 participants (60%). Most participants (57; 87.7%) enjoyed the online classes, but 53 (81.5%) rated them as less fun than the real classes as direct contact was missing. Well-being increased significantly after the lesson and remained improved for several days. CONCLUSION: Transforming a dancing class is feasible for participants with digital experience and goes along with technical difficulties. It is a substitute for real classes if mandatory and improves well-being.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".