The co-development and evaluation of an e-learning course on spinal cord injury physical activity counselling: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Health, fitness and lifestyle professionals can play important roles in promoting physical activity in groups at risk of developing an inactive lifestyle, such as people with spinal cord injury (SCI). Tailored counselling is a promising tool to promote and improve physical activity levels. To support professionals to effectively have a conversation about physical activity with clients with SCI, evidence-based training and resources are needed. This project aimed to (1) co-develop an e-learning course on best practices for SCI physical activity counselling and, (2) examine the effectiveness and usability of this course. METHODS: Guided by the technology-enhanced learning (TEL) evaluation framework, we used a systematic, multistep approach to co-develop and evaluate an e-learning course. The development process was informed by input and feedback from a diverse group of end-users and experts (n > 160) via online surveys and (think-aloud) interviews. A randomized controlled trial was used to compare learning outcomes (post-knowledge and self-efficacy) between participants who completed the course (intervention group) and the wait-listed control group. Usability, learning experiences, and satisfaction were assessed among all participants. RESULTS: Forty-one participants (21 intervention-group; 20 control-group) with various backgrounds (e.g., lifestyle counsellors, physiotherapists, occupational therapists, recreation therapists, fitness trainers) enrolled in the randomized controlled trial. After completing the course, participants in the intervention group showed significantly improved knowledge on the best practices for SCI physical activity counselling and higher self-efficacy for using these best practices in conversations with clients with SCI compared to the control group (p <.001). Participants reported above average usability scores, positive learning experiences, and high levels of satisfaction when completing the course. CONCLUSION: We used a systematic, multi-step, theory-informed approach to co-develop and evaluate an evidence-based e-learning course on SCI physical activity counselling to support professionals to promote physical activity in their daily practices. The overall positive findings demonstrate that the e-learning course is feasible and ready for further implementation in various health and community settings. Implementation of the e-learning course can help professionals improve the physical activity support they provide to their clients, and subsequently increase physical activity participation in people with SCI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".