PREVENTING ADVERSE CONCUSSION OUTCOMES: THE ONLINE CONCUSSION AWARENESS TRAINING TOOLKIT (CATT)
Bibliographic record
Abstract
Background Concussion recognition, treatment and management is crucial in supporting recovery and decreasing the risk of long-term brain damage. Long-term effects are often not recognized early enough to prevent post-concussion syndrome, resulting in an impact on social and professional lives. The need to standardize care is vital in preventing adverse concussion outcomes. Objective To determine if concussion knowledge, attitudes and practices (KAP) are significantly improved among physicians and nurses following completion of CATT. Design A pre/post-intervention questionnaire designed to measure changes in physician/nurse KAP. Setting Primary setting was hospital emergency departments in the Lower Mainland, British Columbia. Participants Physicians/nurses working in emergency departments/trauma care facilities. Intervention Based upon established international principles, CATT is an online toolkit (www.cattonline.com) providing learner-directed concussion awareness training for health practitioners as well as assessment resources (SCAT3, Child-SCAT3), links to clinical resources, patient handouts, journal articles (including the Zurich Consensus Statement), related websites, concussion videos and study cases. Main outcome measures Change in physician/nurse knowledge, attitudes and practices regarding concussion recognition, treatment and management. Results 44 physicians and 35 nurses were recruited. Post-intervention questionnaires were completed by 34 physicians (77.3%) and 25 nurses (71.4%). Physicians demonstrated a statistically significant positive change in concussion practices (P=.001). Change in physician knowledge was not significant, while attitudes had a negative change (P=.041). Positive change in physician knowledge was detected for those who typically see more than 10 concussions per year (P=.039). Nurses demonstrated statistically significant positive change in practices (P=.005) and attitudes (P=.035), but no change in knowledge. Conclusions CATT is effective in improving concussion knowledge and practices among physicians and nurses, which will potentially minimize adverse concussion outcomes and lower health care costs among concussion patients. Phase 2 includes a toolkit for Parents, Players and Coaches, currently undergoing an evaluation with sporting associations in BC.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".