Tracking outcomes of activity-based therapy after spinal cord injury: a qualitative study of current practice in Canada
Bibliographic record
Abstract
PURPOSE: Activity-based therapy (ABT) promotes neuromuscular activation below the injury level in individuals with spinal cord injury (SCI). This qualitative descriptive study explored the experiences and perceptions of Canadian clinicians and clinic administrators regarding assessment practices in community-based ABT programs. METHODS: Twelve participants from six community clinics, in four provinces, completed individual interviews that followed a semi-structured guide informed by the Theoretical Domains Framework. These interviews explored current ABT assessment practices, needs, benefits and challenges. Interviews were audio-recorded and transcribed verbatim. Using interpretive description, and guided by the DEPICT model, data were collaboratively analyzed to identify an overarching theme and categories. RESULTS: One overarching theme was identified: ABT is a developing field that lacks standardized guidelines for assessment practices. Four categories were identified within this theme: factors influencing clinician's decisions about ABT assessment practice, challenges with current ABT assessment practice, advantages of current assessment practice, and progression of assessment for ABT programs. Participants would like standardized assessment tools and improved awareness of and access to assessments for ABT programs. CONCLUSIONS: Canadian community-based ABT clinics lack standardized assessment practices. Leadership and collaboration are needed to develop and implement assessment guidelines to support research and advocacy for ABT after SCI.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".