Development of a tailored concussion education program for athletes : a pragmatic multimethods design and integrated knowledge translation approach from needs assessment to design
Bibliographic record
Abstract
Objectives To understand Canadian university athletic programme concussion management needs, and to describe development and content of a tailored online concussion education tool for Canadian university/college athletes. Design An integrated knowledge translation multiphased, multimethods approach was used. Phases included a needs assessment survey with university representatives and athletes, content selection, mapping behavioural goals to evidenced-based behaviour change techniques, script/storyboard development, engagement interviews with university athletes and tool development using usercentred design techniques. Setting Canadian U SPORTS universities (n=56). Participants Overall, 64 university representatives (eg, administrators, clinicians) and 27 varsity athletes (52% male, 48% female) completed the needs assessment survey. Five athletes participated in engagement interviews. Outcome measures Surveys assessed previous athlete concussion education, recommendations for concussion topics and tool design, concussion management challenges and interest in implementing a new course. Results Institutions used a median (Med) of two (range 1–5) approaches when educating athletes about concussion. Common approaches were classroom-style education (50%), online training (41%) and informational handouts (39%). University representatives rated most important topics as: (1) what is a concussion, (2) how to recognise a concussion and (3) how to report a concussion (Medall=4.8/5). Athletes felt symptom recognition (96%) and effects on the brain (85%) were most important. The majority of athletes preferred learning via computer (81%) and preferred to learn alone (48%) versus group learning (7%). The final resource was designed to influence four behaviours: (1) report symptoms, (2) seek care, (3) encourage teammates to report symptoms and (4) support teammates through concussion recovery. Examples of behaviour change techniques included: knowledge/skills, problem-solving scenarios, verbal persuasion and social comparison. Athletes are guided through different interactions (eg, videos, flip cards, scenarios, testimonials) to maximise engagement (material review takes ~30 min). Conclusions The Concussion Awareness Training Tool for athletes is the first Canadian education tool designed to address the needs of Canadian university/college athletes.
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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 | 0.038 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".