Bibliographic record
Abstract
Objective To support standardise concussion recognition, diagnosis, treatment and management through an evaluated online resource based upon the Zurich Consensus Statement on Concussion in Sport and other evidence-based resources. The online Concussion Awareness Training Tool (CATT–www.cattonline.com) includes three free toolkits providing training for: Medical Professionals–MP; Parents, Players and Coaches–PPC; School Professionals–SP. Design Pre-post intervention survey with sample size of at least 33 for power to detect a large effect size of 0.5. Setting British Columbia, Canada. Participants MP: 42 physicians and 33 nurses practising in BC; PPC: 35 parents in BC with a child registered in organised sport; SP: 43 K-12 teachers working in BC. Intervention CATT MP aims to standardise practice in a clinical setting with a focus on the paediatric patient. CATT PPC speaks to concussion identification and management, with Smartphone accessible resources: Concussion Response Tool and Questions to Ask Your Doctor. CATT SP includes Return-to-Learn protocol and resources for teachers, administrators, counsellors and others in the school setting. Outcome measures Change in knowledge, attitudes and practices. Results CATT MP was launched April 2013. Physicians demonstrated significant positive change in concussion practices (p=0.001), and significant change in knowledge by those treating more than 10 concussions per year (p=0.039). Nurses had significant positive change in practices (p=0.005) and attitudes (p=0.035). CATT PPC was launched June 2014. Parents demonstrated significant positive change in concussion knowledge (p=0.002). CATT SP was launched February 2016; evaluation in process. Conclusions CATT increases concussion knowledge and awareness among target audiences. 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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".