12.2 Physician concussion knowledge, patterns of practice and learning preferences: a cross-sectional correlate survey study between 2013 and 2022
Bibliographic record
Abstract
Objective Characterize physician concussion knowledge, patterns of practice and learning preferences in the Sections of General & Family Practice (GFP) and Sport and Exercise Medicine (SEM) of the Ontario Medical Association (OMA). Design Cross-sectional online survey, first distributed in 2013, repeated in 2022. Setting Self-reported survey in Ontario, Canada. Participants Active physicians within the OMA Sections of GFP and SEM seeing patients with concussion. Response rates: 2013: GFP 225/12,168 (1.8%), SEM 85/594 (14.3%); 2022: GFP 216/15,674 (1.4%), 35/696 (5.0%). Interventions Independent variables were time (2013 vs. 2022) and Section (GFP vs. SEM). Outcome Measures Concussion guideline usage, assessment tool use, management, and preferred learning methods. Main Results Non-reliance on published guidelines decreased overall from 2013 to 2022; non-reliance was higher among GFP physicians for both surveys (2013: GFP- 38.2%, SEM 8.2%; p<0.001; 2022: GFP- 23.7%, SEM- 2.9%; p=0.003). Sport Concussion Assessment Tool use increased for initial assessment (GFP: 2013- 34.2%, 2022- 65.0%; p<0.001; SEM: 2013- 68.2%, 2022- 90.9%; p=0.010) and return-to-play decisions (GFP: 2013- 29.8%, 2022- 56.1%; p<0.001; SEM: 2013- 61.2%, 2022- 85.3%; p=0.016). Physical and cognitive rest recommendations shifted from complete rest to subthreshold/modified activities over time (p<0.001 for both). Preferred resources identified for future learning were websites (46.2%) and continuing medical education (85.0%). Conclusions Comparison between the 2013 and 2022 surveys revealed improvements in physician knowledge levels and patterns of practice, but gaps between Sections remain. Future work should utilize a validated tool in a larger cohort to compare physician-reported knowledge and attitudes with behaviours observed in practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".