Delivering concussion education to pre-service teachers through the SCHOOLFirst website: Evaluating usability and satisfaction
Bibliographic record
Abstract
BACKGROUND: Return-to-school processes indicate 'when' to initiate activities and 'what' activities should be accomplished, but are missing 'how' to implement the process. The SCHOOLFirst website provides the 'how' through building concussion knowledge, creating a supportive culture, and defining roles. Due to the involvement of pre-service teachers in schools during training and imminent transition to becoming teachers, it is important that pre-service teachers are trained in concussion and can optimally support current and future students. OBJECTIVE: To determine: 1) pre-service teachers' knowledge and confidence surrounding the return-to-school process before and after using the SCHOOLFirst website; 2) the usability, intended use and satisfaction of the SCHOOLFirst website from the perspective of pre-service teachers. METHODS: Thirty pre-service teachers completed the demographic survey, knowledge and confidence survey, System Usability Scale, and satisfaction and intended use survey after participating in a workshop. RESULTS: Significant increases in concussion knowledge (Z = -4.093, p < 0.001) and confidence in helping students return-to-school (Z = -4.620, p < 0.001) were measured after using the SCHOOLFirst website. Participants were satisfied with the SCHOOLFirst website (93.4%) and intend to use it in the future when supporting a student post-concussion (96.4%). CONCLUSION: The SCHOOLFirst website is a valuable tool for pre-service teachers to support students' return-to-school post-concussion.
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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 | 0.006 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".