Clinician Perspectives on Providing Concussion Assessment and Management via Telehealth: A Mixed-Methods Study
Bibliographic record
Abstract
OBJECTIVE: To examine clinician perspectives regarding the use of telehealth for concussion assessment and management. SETTING: A Pan-Canadian survey. PARTICIPANTS: Twenty-five purposively sampled multidisciplinary clinician-researchers with concussion expertise (female, n = 21; physician, n = 11; and other health professional, n = 14). DESIGN: Sequential mixed-method design: (1) electronic survey and (2) semistructured interviews with focus groups via videoconference. Qualitative descriptive design. MAIN OUTCOME MEASURES: Survey : A 59-item questionnaire regarding the suitability of telehealth to perform recommended best practice components of concussion assessment and management. Focus groups : 10 open-ended questions explored survey results in more detail. RESULTS: Clinicians strongly agreed that telehealth could be utilized to obtain a clinical history (96%), assess mental status (88%), and convey a diagnosis (83%) on initial assessment; to take a focused clinical history (80%); to monitor functional status (80%) on follow-up; and to manage symptoms using education on rest (92%), planning and pacing (92%), and sleep recommendations (91%); and to refer to a specialist (80%). Conversely, many clinicians believed telehealth was unsuitable to perform a complete neurologic examination (48%), cervical spine (38%) or vestibular assessment (61%), or to provide vestibular therapy (21%) or vision therapy (13%). Key benefits included convenience, provision of care, and patient-centered approach. General and concussion-specific challenges included technology, quality of care, patient and clinician characteristics, and logistics. Strategies to overcome identified challenges are presented. CONCLUSIONS: From the perspective of experienced clinicians, telehealth is suited to manage symptomatic concussion patients presenting without red flags or following an initial in-person assessment, but may have limitations in ruling out serious pathology or providing return-to-sport clearance without an in-person physical examination.
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 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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".