Education in action: evaluating kids concussion infographics
Bibliographic record
Abstract
Objective To assess the impact of infographics on enhancing concussion knowledge and their effectiveness as a knowledge translation (KT) strategy. Design Prospective, post-survey design to assess a KT strategy. Setting Community and hospital based education events. Participants Individuals across various stakeholder groups were invited to provide feedback on six infographics. Data (N=106) was collected from youth (52%) and adults (48%), representing five stakeholder groups: athletes, students, teachers, healthcare trainees and healthcare professionals. Intervention Six infographics were created to provide salient information about concussion for a multi-stakeholder audience. Outcome measures A survey was designed to gather information about the value and utility of the infographics as a KT strategy and to determine additional knowledge needs. Main results Ninety percent of participants identified that the infographics met their knowledge needs, and 84% of participants indicated that the infographics gave them new knowledge related to sleep, myths and facts, and signs and symptoms. Participants indicated that they intend to use the infographics to build knowledge (91%), educate others (52%), and cope with concussion (26%). Feedback provided about the infographic format was positive, and suggestions for future topics were offered. Conclusions Infographics are a potentially effective KT strategy that meets knowledge needs and appeals to many different audiences. Participants identified that they want to learn more about areas including concussion recovery and management. Data collection is ongoing and continues to inform the value of infographics as a KT strategy. This study is part of a broader education initiative to optimise paediatric concussion knowledge. 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 | 0.014 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".