Exploring the impact of dance: intersectoral quantitative and qualitative methodological challenges, lessons learned, and recommendations
Bibliographic record
Abstract
Dance-based interventions/programs are gaining popularity; however, interventions are rarely explicitly described, including the role of dance facilitators, and their effects can be difficult to measure. METHODS: a non-randomized pre-post study for six dance groups (teenagers with cerebral palsy, children with cerebral visual impairment, adults receiving outpatient physical rehabilitation, adults with Parkinson's disease, women who were formerly unsheltered, and community-dwelling older adults) outcome measures (heart rate variability-HRV, Multidimensional Outcome Expectation for Exercise Scale, Physical Activity Enjoyment Scale, Flow Sate Scale, and an in-house questionnaire) were collected with 34 participants up to five times to explore changes over time. Interviews, ethnographic observations, video recording and a qualitative thematic analysis were also conducted to describe the pedagogical strategies of one dance facilitator. RESULTS: HRV data were deemed unusable and other quantitative outcomes did not demonstrate statistically significant trends. Qualitative thematic analysis revealed important information about the adaptive verbal and non-verbal interactions between the facilitator and participants, linking to pleasure, effort, and body engagement. DISCUSSION/CONCLUSION: Even without significant trends quantitatively, results were encouraging, and qualitative analyses were illuminating. Lessons learned and recommendations for future dance research and policymakers are included.
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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 | 0.436 | 0.324 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".