The MyGuide Web-Based Self-Management Tool for Concussion Rehabilitation: Mixed Methods Cross-Sectional Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Web-based concussion self-management education programs for adolescents can improve functional outcomes, reduce concussion symptoms, and increase self-efficacy. However, there are a limited number of studies examining the perceptions and acceptance of these programs and the use of these tools in the adult concussion population. OBJECTIVE This study aimed to investigate the perceptions and acceptance of clinicians and adults with concussions using MyGuide Concussion (Vancouver Coastal Health), a web-based concussion self-management tool. METHODS Using a mixed methods sequential explanatory design, a convenience sample of 8 adults with concussions and 8 clinicians who used MyGuide Concussion over a 2-year period were interviewed, and their responses were analyzed. RESULTS Participants reported two key benefits of using the web-based self-management tool: (1) the tool’s emphasis on the interconnectedness of physical and psychological symptoms, and (2) the ability to provide reassurance that symptom being experienced were a normal part of the concussion experience. Clinicians described the tool as being useful as a supplementary source of information for clients in addition to clinical sessions and believed the content was useful for increasing clients’ independence in managing their own recovery. CONCLUSIONS Overall, the evaluation of the MyGuide tool is an acceptable and well-perceived tool for adults with concussions who require a basic understanding of concussion recovery, particularly in the early stages of recovery. Future research may include optimizing MyGuide by targeting promotional strategies and addressing other barriers to use.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".