Development of an activity-based therapy tracking tool: Item generation and reduction using the Delphi method
Bibliographic record
Abstract
Activity-based therapies (ABT) are increasingly used in rehabilitation after spinal cord injury or disease (SCI/D). However, the absence of standardized tools to track the details of an ABT program hinders the collection of data needed for client-tailored programming and resource allocation. The objective of this study is to determine the content to include in an ABT tracking tool for people living with SCI/D. Cross-sectional e-survey. Community. The 60 participants from Canada and the United States who had knowledge and/or experience with ABT included: individuals with SCI/D; hospital clinicians (i.e. physical and occupational therapists/assistants); community-based clinicians; hospital or community clinic administrators; researchers; and funders, advocates and policy makers. None. A Delphi e-survey comprised 16 types of ABT (e.g. treadmill training) and 4 types of technology (e.g. virtual reality). Participants rated the importance of including each item on a tracking tool and the feasibility to track each item using a 9-point Likert scale. After two survey rounds, nine types of ABT and one technology were identified as important to include in a tracking tool. All items rated as important were considered feasible for clinicians and people with SCI/D to track, except crawling. This study identified the types of ABT and technology to include in an ABT tracking tool. Such a tool may provide details of an ABT program that can support decision-making at the individual, program and health system levels and aid the development of best practice guidelines.
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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 | 0.103 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".