Tracking activity-based therapy for people living with spinal cord injury or disease: insights gained through focus group interviews with key stakeholders
Bibliographic record
Abstract
PURPOSE: The development of a tool to track participation in activity-based therapy (ABT) for people with spinal cord injury or disease (SCI/D) was identified as a priority of the Canadian ABT Community of Practice. The objective of this study was to understand multi-stakeholder perspectives on tracking ABT participation across the continuum of care. MATERIALS AND METHODS: Forty-eight individuals from six stakeholder groups (persons living with SCI/D; hospital therapists; community trainers; administrators; researchers; and funders, advocates and policy experts) were recruited to participate in focus group interviews. Participants were asked open-ended questions concerning the importance of and parameters around tracking ABT. Transcripts were analyzed using conventional content analysis. RESULTS: Themes reflected the Who, What, Where, When, Why and How of tracking ABT. Participants described the importance of involving hospital therapists, community trainers and individuals with SCI/D in tracking ABT to capture both subjective and objective parameters across the continuum of care and injury trajectory. Digital tracking tools were favoured, although paper-based versions were regarded as a necessity in some circumstances. CONCLUSIONS: Findings highlighted the importance of tracking ABT participation for individuals with SCI/D. The information may guide the development of ABT practice guidelines and support the implementation of ABT in Canada.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".