Theory- and evidence-based best practices for physical activity counseling for adults with spinal cord injury
Bibliographic record
Abstract
This project used a systematic and integrated knowledge translation (IKT) approach to co-create theory- and evidence-based best practices for physical activity counseling for adults with spinal cord injury (SCI). Guided by the IKT Guiding Principles, we meaningfully engaged research users throughout this project. A systematic approach was used. An international, multidisciplinary expert panel (n = 15), including SCI researchers, counselors, and people with SCI, was established. Panel members participated in two online meetings to discuss the best practices by drawing upon new knowledge regarding counselor-client interactions, current evidence, and members’ own experiences. We used concepts from key literature on SCI-specific physical activity counseling and health behavior change theories. An external group of experts completed an online survey to test the clarity, usability and appropriateness of the best practices. The best practices document includes an introduction, the best practices, things to keep in mind, and a glossary. Best practices focused on how to deliver a conversation and what to discuss during a conversation. Examples include: build rapport, use a client-centred approach following the spirit of motivational interviewing, understand your client’s physical activity barriers, and share the SCI physical activity guidelines. External experts (n = 25) rated the best practices on average as clear, useful, and appropriate. We present the first systematically co-developed theory- and evidence-based best practices for SCI physical activity counseling. The implementation of the best practices will be supported by developing training modules. These new best practices can contribute to optimizing SCI physical activity counseling services across settings.
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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 | 0.140 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".