Concussion Management Practices for Youth Who Are Slow to Recover: A Survey of Canadian Rehabilitation Clinicians
Bibliographic record
Abstract
Purpose: The objective of this study was to estimate the scope of concussion management practices for youth used by Canadian rehabilitation clinicians. A secondary objective was to determine the use of aerobic exercise as a management strategy. Method: Members of the Canadian Association of Occupational Therapists, Canadian Athletic Therapists Association, and Canadian Physiotherapy Association were invited to participate in an online cross-sectional survey. Two clinical vignettes were provided with a brief history. The respondents were asked about the type of treatments they would provide (e.g., manual therapy, education, aerobic exercise, return-to-learn or return-to-play protocol, goal setting). Results: The survey was completed by 555 clinicians. The top five treatment options were education, sleep recommendations, goal setting, energy management, and manual therapy. Just more than one-third of the clinicians prescribed aerobic exercise. Having a high caseload of patients with concussion (75%–100%) was a significant predictor of prescribing aerobic exercise. Conclusions: A wide variety of treatment options were selected, although the most common were education, sleep recommendations, energy management, and goal setting. Few clinicians used aerobic exercise as part of their concussion management strategy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".