Professionals’ knowledge, skills and confidence on using the best practices for spinal cord injury physical activity counseling in Canada and the Netherlands
Bibliographic record
Abstract
Context To improve physical activity (PA) participation in people with spinal cord injury (SCI), an international panel co-created theory- and evidence-based best practices for SCI PA counseling. This study aimed to identify and compare Canadian and Dutch counselors’ knowledge, skills, and confidence in using these best practices.Methods An online survey was conducted in Canada and the Netherlands. Respondents were included if they worked or volunteered as exercise/lifestyle counselor, recreation therapist, physiotherapist, occupational therapist, or peer mentor and were planning to provide counseling in the next 12 months. Chi-square tests, t-tests and linear regression analyses were used to compare groups.Results Canadian (n = 45) and Dutch respondents (n = 41) had different expertise, with the majority of Canadians working as therapeutic recreation therapist and the majority of Dutch respondents working as PA/lifestyle counselor. In both countries, respondents scored relatively high on their knowledge, skills, and confidence in using the best practices on how to have a conversation and what to discuss during a conversation. Dutch respondents scored slightly higher in their confidence for using best practices about building rapport, motivational interviewing, and tailoring the support (p = 0.05).Conclusions The generally high counseling skills reported by Canadian and Dutch respondents may be due to the history of SCI-specific PA promotion projects conducted in both countries. These survey findings were used to inform the development of evidence-based training modules on SCI PA counseling. This study may inspire cross-country collaboration and exchange to optimize the organization and delivery of PA counseling services for adults with SCI.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".