Dancing for Brain Health and Mobility: A Mixed-Methods Evaluation of the Implementation of Virtual GERAS DANCE Across Diverse Settings
Bibliographic record
Abstract
This study examines the implementation of virtual GERAS DANCE in community, hospital, and long-term care (LTC) settings. Using the Consolidated Framework for Implementation Research (CFIR), we conducted surveys and semi-structured interviews. Key CFIR constructs analyzed included innovation characteristics, outer setting, inner setting, individual characteristics, and implementation process. We gathered feedback from 22 healthcare professionals across 11 sites in five practice settings. Virtual GERAS DANCE reached 135 older adults. Implementation was most influenced by innovation characteristics (enjoyable music, socialization, exercise, and evidence-based design) and inner setting factors within participating organizations (cost-effective implementation, experienced staff in virtual programs, and high demand for virtual options). Barriers included technical limitations such as limited Wi-Fi capacity in rural communities. The success of virtual GERAS DANCE was largely driven by its evidence-based design and the organizational readiness for virtual health interventions.
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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 | 0.024 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".