Current Use and Future Considerations for Concussion Telemedicine Healthcare in Canada
Bibliographic record
Abstract
OBJECTIVE: To examine the use of telemedicine among Canadian concussion providers and clinics before and after the COVID-19 pandemic onset and identify barriers and facilitators for future use. METHODS: Ninety-nine concussion clinics and healthcare providers across Canada that offered one or more clinical concussion-related service were identified using standardized online searches and approached to complete a cross-sectional online survey. RESULTS: Thirty clinics or providers completed the survey and two completed subsections of the survey (response rate of 32.3%). Only 28.1% of respondents indicated that they used telemedicine to provide care prior to the COVID-19 pandemic. Providers most commonly using telemedicine prior to the pandemic were occupational therapists and physicians, while the most commonly used services were in-person videoconferencing and eConsultation. Most respondents (87%) indicated their clinic's use of telemedicine changed following the onset of the COVID-19 pandemic including new use of in-person video-conferencing, telephone calls, and eConsultation. Ninety-three percent indicated that they would consider using telemedicine to provide care to their concussion patients once the pandemic was over. Barriers needed to be overcome to facilitate use or greater use of telemedicine-based services were the inability to conduct a complete physical examination, lack of appropriate reimbursement, lack of start-up, and maintenance funding and medico-legal risk. CONCLUSION: Telemedicine was used by a minority of Canadian concussion clinics and providers prior to the COVID-19 pandemic but was rapidly adopted by many facilities. This study provides important insight into the factors that must be considered to optimize use of telemedicine in concussion care in the future.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".