Adapted Dance for Post-Stroke Patients
Bibliographic record
Abstract
This communication/workshop presents the development process of \nan adapted dance intervention for people affected \nby a combination of neurological deficits resulting \nfrom a recent stroke (≤ 25 days). The intervention \nwas part of a study conducted at the Villa Medica \nRehabilitation Hospital in Montreal. Its objectives \nwere to intensify – in a pleasant fashion – the rehabilitation \nof participating patients, and to contribute \nto their recovery by creating a connection between \nthe multimodal nature of dance and the needs and \nrecommendations of the multidisciplinary hospital \nneurology team. This workshop introduces categories \nof permeable movements that result from the \naforementioned connection. These categories borrow \nelements from rehabilitation, dance and somatic \neducation practices, in order to form an intervention \ncontent that's both relevant to, and coherent with, \nthe therapeutic intention. The workshop, in so doing, \naims to respond to current literature advocating for a \nbetter understanding of intervention contents – much \nof which is still not clearly formulated when it comes \nto intervention through dance. The workshop begins \nwith a description of the methodology (patient medical \ninformation and questionnaire, discussion group, \non-site observations and TIDieR checklists), followed \nby the introduction of different movement categories, \na practical/experiential synthesis of developed \ncontent (in sitting and standing positions), and finishing \nwith a short montage of audio-visual excerpts \nshot in the field to help further examine the content. \nThe workshop concludes with a short question and \nanswer period.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".