70 Preventing adverse concussion outcomes: the online concussion awareness training toolkit
Bibliographic record
Abstract
Statement of purpose Prevention, recognition and treatment of concussion, particularly in sport, is important nationally and internationally. The online Concussion Awareness Training Toolkit (CATT) was developed to standardise diagnosis, treatment and management. Methods/approach Based upon the Zurich Consensus Statement on Concussion in Sport, CATT is a resource designed for specific audiences. The first, for health practitioners (CATT HP) aims to standardise practice in a clinical setting. CATT HP includes a self-paced learning module; diagnostic tools; clinical resources, patient handouts, journal articles, websites, videos and individual case studies. The second tool is specific to parents, players and coaches (CATT PPC) and speaks to the identification and appropriate management of concussion, including smartphone accessible resources: ‘concussion response tool’ and ‘questions to ask your doctor’. The final tool is for educators (CATT E). It includes the return-to-learn protocol and resources for teachers, administrators, counsellors, aides. Results CATT HP was launched mid April, 2013. To date, it has received 28,000 hits worldwide and has been evaluated using a pre/post intervention self-report survey. Physicians demonstrated significant positive change in concussion 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 and evaluation data is currently being analysed. Results will be prepared by December, 2014. Conclusion Concussion is an under-recognised, -diagnosed and -treated medical condition, requiring both physical and mental rest. The CATT addresses this gap by increasing knowledge and awareness among appropriate specific audiences. Significance and contributions Good concussion management can reduce related health problems and the risk of long-term brain damage, potentially lowering total health care costs among those who are injured.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".