Development of a KT intervention to promote the use of dance as a therapeutic modality in rehabilitation
Bibliographic record
Abstract
Background: Dance is a modality of choice in rehabilitation, because it combines the benefits of aerobic exercise with an enjoyable social activity.Despite the scientific evidence supporting the use of dance in rehabilitation, this treatment modality is rarely used in rehabilitation settings.As with other evidence--based interventions in rehabilitation, the implementation of scientific results from research to practice remains a challenge.Objective: This thesis aims to answer the following research question: What knowledge translation (KT) strategy will enable professionals working in rehabilitation to implement a specific dance intervention as a therapeutic modality?To answer this question, the two manuscripts presented in this thesis will address the following specific research objectives:1) To determine the factors influencing the implementation of a dance intervention in a rehabilitation setting, 2) To identify the preferred format for a KT strategy based on clinicians' perceptions of their needs for specific knowledge. Methods: Using a descriptive qualitative study design, this research project was based onthe Knowledge--to--Action Process.Three focus groups were conducted with allied health-care professionals from three purposefully selected rehabilitation centers of the Centre for Interdisciplinary Research in Rehabilitation of the Greater Montreal (CRIR).Two members of the research team independently analyzed the transcripts using thematic content analysis.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".