What are the knowledge, attitudes and beliefs regarding concussion of primary care physicians and family resident physicians in rural communities?
Bibliographic record
Abstract
Background Primary care physicians and family medicine resident physicians report continued gaps in knowledge when diagnosing and managing pediatric patients with concussion. Methods A cross-sectional electronic survey of 130 primary care physicians and family medicine resident physicians in the Northeastern Ontario Local Health Integration Network (LHIN). Descriptive statistics, chi-squared Fisher exact tests, were used to compare physicians versus resident physicians with two-tailed p < 0.05 (with 95% confidence intervals). Results With a 48% response rate, when treating concussions 44% of providers either did not use any specific clinical practice guideline, standardized assessment tool, could not recall the source of a specific tool/guideline or omitted answering the question. However, 61% of all respondents would refer some or all concussion patients to a specialist for treatment. At least 41% of providers indicated they lacked access to a ‘Provider Decision Support Tool’ specific to concussion, and 88% of the 25 providers were without access to discharge instructions. Conclusion Similar to other jurisdictions, Northeastern Ontario primary care physicians and family medicine resident physicians report gaps in knowledge for both diagnosis and management of pediatric concussion. Consequently, they did not use current guidelines or best practices to guide management.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".