Describing High School Stakeholders' Preferences for a Return‐to‐School Framework Following Concussion
Bibliographic record
Abstract
BACKGROUND: Return to school supports are recommended to facilitate adolescents' re-entry to school following a concussion. However, little is known as to what school stakeholders prefer for a return-to-school process. This study sought to describe the preferences of high school students, parents, and educators for a Return-to-School Framework for adolescents following a concussion. METHODS: We conducted qualitative semi-structured, 1-on-1 or group interviews with high school students (n = 6), parents (n = 5), and educators (n = 15) from Calgary, Canada. Interviews aimed to describe participants' preferences for a Return-to-School Framework for students following a concussion. Interviews were analyzed using conventional content analysis. RESULTS: We organized the data into 4 main themes: (1) purpose of the Return-to-School Framework; (2) format and operation of the Return-to-School Framework; (3) communication about a student's concussion; and (4) necessity of concussion education for students and educators. IMPLICATIONS FOR SCHOOL HEALTH POLICY, PRACTICE, AND EQUITY: A Return-to-School Framework following concussion should be developed in consultation with families, educators, and students and supports should be tailored to each student. CONCLUSIONS: Participants preferred a standardized and consistent Return-to-School Framework including ongoing communication between stakeholders as well as feasible and individualized school supports.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".