Use of Participatory Action Research in the Development of a Survey of Physiotherapy Services for People with Multiple Sclerosis in Canada
Bibliographic record
Abstract
Purpose: Currently, there is a paucity of research describing physiotherapy services for individuals with multiple sclerosis (MS) in Canada. Using qualitative methods, we aimed to develop a survey to examine physiotherapy practice patterns for people with MS receiving services in Canada. Method: We began by conducting a review of the current literature and combining participatory action research methods with the expertise of registered physiotherapists and individuals with MS. Semi-structured interviews were conducted with 10 participants to obtain their input into survey development. The interviews were then transcribed verbatim and analyzed thematically. Results: Five key themes emerged from the thematic analysis: (1) provide additional answer options, (2) reformat or clarify questions, (3) ensure that questions or options are appropriate, (4) ensure good readability and flow, and (5) determine the appropriate length of the survey. After a final revision, the survey consisted of 24 items in the following domains: demographics, MS programme and patient population, interdisciplinary care, and programme and service barriers. Conclusions: This survey is the first of its kind in Canada and is the first step toward improving the quality of health of people living with MS and the effectiveness of current physiotherapy practices for them.
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 | 0.182 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".