Pilot survey regarding “the green folder” – an educator-designed secondary school return to learn protocol after a sport-related concussion
Bibliographic record
Abstract
Objective To demonstrate whether a user-centred (teacher accepted and student focused) return to learn (RTL) protocol after a sport-related concussion (SRC)has an efficacious role. Design Prospective pilot survey. Setting Four sport medicine clinics in Ontario, Canada. Participants Twenty-eight collegiate athletes (21 males and 7 females: age=20.2 ± 1.7 years, height=182.2 ± 11.9 cm, mass=90.3 ± 24.2 kg) without history of: headaches or migraines, substances/alcohol abuse, and/or psychiatric conditions. Intervention NineSRC students from one (index) school who used the new protocol and a control group (11 SRC students) from other schools, who did not use the new protocol,completed a web-based survey. Outcome measures Primary outcome measures include student perception of school support during their SRC RTL. Main results Twenty surveys were completed from an eligible 52 students (38.5%).Among students who received the Green Folder at the index school, 71.4% either strongly or mostly agreed thatsuch an RTL protocol could shorten the duration of SRC symptoms. At that school, 100% strongly or mostly agreed (versus 14.3% from the other schools) that the school was very helpful advising about the RTL steps and when they should return to school. 85.7% of students at the index school versus 28.6% from other schools, strongly or mostly agree that the school staff fully understands RTL strategies after SRC. Conclusions The “Green Folder” incorporating RTL steps and a daily class-by-class accommodation sign off by a guidance counsellor, appears to have perceived efficacy for SRC students. Further research will determine whether such a user-centred RTL protocol results in a more efficacious implementation of expert recommendations about concussion management, worthy of widespread dissemination. 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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".