Stakeholder Recommendations to Increase the Accessibility of Online Health Information for Adults Experiencing Concussion Symptoms
Bibliographic record
Abstract
Background: Concussion is a global public health problem. In Canada, concussion is among the top five reasons for workplace time-loss. Concussion results in physical, cognitive, and/or emotional symptoms that temporarily worsen with physical and mental exertion, such as viewing electronic screens. The Internet is the primary source of consumer health information. Studies on the end-user needs of adults with brain injuries in regards to digital health technologies largely focus on informational content. There is little to no research on the accessibility of screen-based informational websites and smartphone applications among this population. Objective: The aim of this research was to involve stakeholders in the design of a comprehensive educational resource to guide concussion recognition, recovery, and return-to-work, called the Concussion Awareness Training Tool for Workers and Workplaces (CATT WW). In order to ensure both relevant content and appropriate delivery of the information to the target groups, participants were asked whether adaptations could increase the accessibility of online health information for the general adult population experiencing concussion symptoms. Methods: Data have been generated through semi-structured in-depth interviews and focus groups with participants from across British Columbia (BC): workers from various industries who were in the concussion recovery process or had returned to work ( n = 31); and healthcare or workplace professionals who support concussion diagnosis, recovery, and return-to-work ( n = 16). Data were analyzed using NVivo 12. Before commencing data collection, ethical permission was granted by the University of British Columbia Research Ethics Board (H18-00604), and approval was received from WorkSafeBC Research Services. Results: Participants ( n = 47) recommended twenty adaptations or supplements to electronic screen-based digital health technologies. Conclusion: Given the high prevalence of concussion among the working adult population, the symptom exacerbation commonly caused by prolonged use of electronic screens, and the demand for online educational resources, these findings can guide clinicians, researchers, technology developers, employers, and occupational health and safety committees to further support adults in concussion recovery and return-to-work.
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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 | 0.038 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".