Exploring the value of the SCHOOLFirst return-to-school resource
Bibliographic record
Abstract
OBJECTIVE: To cocreate an evidence-based resource to enable educators to support students returning to school after concussion; evaluate the usability of and users' satisfaction with the resource; understand the role of the resource in supporting students' return to school; and describe changes in concussion knowledge following a concussion education and training workshop. DESIGN: Survey during a concussion education and training workshop. SETTING: Holland Bloorview Kids Rehabilitation Hospital in Toronto, Ont, and York Region District School Board in Richmond Hill, Ont. PARTICIPANTS: Fifty-six educators, of whom 64% were teachers, 11% were school administrators, 23% fulfilled other roles (eg, child and youth worker), and 2% fulfilled unspecified roles. MAIN OUTCOME MEASURES: The survey collected demographic information, usability data via the System Usability Scale, and satisfaction data. Thematic analysis was used for open-ended questions. RESULTS: Participants reported the resource to be easy to use (69.6%), not complex (62.5%), and most felt confident using this resource (83.9%). Participants indicated they were satisfied with the resource (73.2%) and would use it in the future (87.5%). Some found the resource overwhelming and recommended it be summarized in a reference guide. Participants found the links, videos, and classroom accommodations or academic supports to be helpful. CONCLUSION: SCHOOLFirst is an evidence-based, user-driven resource that was created for educators to support students returning to school following concussion. Educators, health care providers, youth, and families collaborated on developing SCHOOLFirst to improve students' successful return to school following concussion. Educators were satisfied with the resource and saw opportunities to use it to support their students.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".