Investigation of knowledge and attitude about concussion diagnosis and management among family medicine residents
Bibliographic record
Abstract
Objective To assess knowledge, attitude and learning needs about concussion among family medicine residents Design A web-based prospective survey Setting Toronto, Canada. Participants Family medicine residents at the University of Toronto. Intervention A link to the survey was disseminated via e-mail by the Department of Family and Community Medicine post-graduate office at the University of Toronto. Data collection occurred over 5 weeks from January to March 2015 with reminder emails sent out to all 348 family medicine residents at 2 weeks and 4 weeks of the study period. Outcome measures Survey answers to assess knowledge and attitude about concussion. Main results The residents who responded (n=73/348, response rate 21%) scored an average of 5.2 correct answers out of 9 (57.8%) questions regarding the diagnosis and management of concussion. Seventy-one percent of residents who responded did not recognise chronic traumatic encephalopathy and only 63% recognised second impact syndrome as consequences of repetitive concussions. Moreover, 32% of residents did not think that “every concussed individual should see a physician” as part of management. Conclusions We found significant gaps in knowledge surrounding concussion diagnosis and management among family medicine residents. This lack of knowledge should be addressed at both the undergraduate medical education and residency training levels to improve concussion-related care and patient outcomes. Competing interests None.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".