Online concussion resources for Canadian high school aged youth: A systematic search strategy
Bibliographic record
Abstract
Objective: To identify online concussion resources that are suitable for Canadian youth and accurate according to the current International Consensus Statement on Concussion in Sport.Methods: A five-phased systematic search strategy was used to identify concussion resources. This included 1. searching key Canadian stakeholder websites (sport organizations, injury prevention groups, children’s hospitals, governments, public health agencies, and physical and health education associations), 2. pediatric concussion expert consultation, 3. applying inclusion and exclusion criteria, 4. reviewing content for accuracy according to the consensus statement and 5. evaluating resources for readability, usability and suitability.Results: A total of 456 resources were initially identified however, only 32 met the final criteria. Across all resources, 34.6% (n=109) were excluded as they were not designed primarily for youth. Among resources reviewed for content accuracy according to the consensus statement, 53.1% (n=51) were excluded. When evaluating resources for readability, usability and suitability, 28.5% (n=13) were excluded as they were not suitable for youth.Implications: Most concussion resources available online are not written for youth and do not contain accurate information on pediatric concussion. The identified evidence-based resources can ensure that accurate and suitable information is provided to youth in order to enhance their concussion knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.044 | 0.043 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".