Dance and Somatic-Informed Movement in an Acute Inpatient Stroke Unit
Bibliographic record
Abstract
Background and Objectives: Stroke units rely on interdisciplinary teams. Professionals with complementary alternative practices may join the team since such approaches are increasingly supporting the stroke recovery process. The aim of this study was to develop a better understanding of how a dance and somatic-informed movement intervention could be utilized in an inpatient setting as an adjunct to post-stroke therapy. We sought to identify (1) what knowledge we could draw on to develop the content and pedagogy for the intervention, (2) what helped/hindered the intervention aimed at functional recovery, as perceived by the practitioner-researchers, and (3) the relationships experienced with the various stakeholders. Materials and Methods: This exploratory qualitative study used the enhanced critical incident technique to collect retrospective self-report data from two practitioner-researchers engaged in delivering the intervention over two months. The data underwent thematic analysis. Patients (n = 6) in a stroke unit were selected within ≤72 h of hospital admission. The intervention was conducted four to six times a week until the vascular neurologist (co-researcher) authorized their transfer to a rehabilitation hospital. Results: The intervention evolved from crafting content and pedagogy at the intersection of different areas of knowledge (dance, somatics, neuroscience, and stroke). It was based on active, assisted, and passive movements. Verbal, tactile, visual, and imaginary inputs used to enhance body awareness were perceived as potentially helping patients recover some range of motion, quality of movements, and voluntary movement control, and fostering calmness and motivation. The intervention was well received by stakeholders. Conclusions: Dance and somatic-informed movement can be a complementary therapy in stroke units, although it requires a delicate juggling of time allocation within the interdisciplinary team. Further studies should be conducted with a larger number of patients and different practitioners. Collaboration between qualitative and quantitative researchers is needed to make a robust case for such interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".