The on-line Concussion Awareness Training Tool (CATT)
Bibliographic record
Abstract
Issue Effects of concussion vary among patients and can greatly affect quality of life. Long-term effects are often not recognized early enough to prevent post-concussion syndrome and permanent brain damage, leading to an impact on social and professional lives. Problem There is need for an accessible, up-to-date, online concussion resource based upon the Zurich Consensus Statement on Concussion in Sport. The Concussion Awareness Training Tool (CATT- www.cattonline.com ) includes three distinct sites for Medical Professionals–MP, Parents, Players & Coaches–PPC, and Educators–Ed. Each site includes a self-paced learning module and tailored resources. CATT MP supports standardize practice in a clinical setting with a focus on the paediatric patient; PPC addresses identification and management; Ed includes resources for those working with children in the school setting. A pre-post intervention survey design was used to evaluate each CATT site, to answer the question: Can CATT change concussion knowledge/attitudes/practices? Results CATT MP was launched mid April 2013. Physicians demonstrated significant positive change in practices (p = 0.001) and significant change in knowledge by those treating more than 10 concussions/yr (p = 0.039). Nurses had significant positive change in practices (p = 0.005) and attitudes (p = 0.035). CATT PPC was launched mid June 2014. Parents demonstrated significant positive change in knowledge (p = 0.002). CATT Ed is anticipated for launch in May 2015, with an accompanying evaluation. Lessons Despite rising concussion awareness, standardized practices have not been widely implemented. Concussion recognition is imperative to influence the extent of damage and recovery from this injury. Public Health can address this gap by promoting concussion training. Key message Concussion is under-recognized, -diagnosed and -treated; CATT can address this gap Good concussion management can reduce related health problems and the risk of long-term brain damage
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.018 | 0.005 |
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".