Online youth concussion resources for Canadian teachers and school staff: A systematic search strategy
Bibliographic record
Abstract
INTRODUCTION: Teachers and school staff (i.e., principals, coaches, trainers, educational assistants, guidance counselors, school healthcare professionals, etc.) are well positioned to support students' return-to-school post-concussion. Teachers and school staff may access concussion resources online as they are readily available; however, their quality and accuracy are unknown. OBJECTIVE: To identify accurate online concussion resources suitable for Canadian teachers and school staff. METHODS: A five-phased systematic search strategy was conducted: 1) initial identification of resources; 2) consultation of pediatric concussion experts; 3) inclusion and exclusion criteria; 4) content review; and, 5) material evaluation. RESULTS: A total of 837 resources were identified initially and 40 resources were included in the final list. Across all resources, 310 (37%) resources were excluded as they were not designed primarily for teachers and school staff. Thirty-four (43%) of 80 resources reviewed for content accuracy were excluded. Among resources reviewed for readability, usability and suitability, six (13%) were excluded. CONCLUSIONS: The 40 resources identified in this study can enable teachers and school staff to educate themselves about concussion and how to optimally support a student's 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 | 0.020 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.051 | 0.043 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".