Academic Accommodations for Middle and High School Students Following a Concussion: Perspectives of Teachers and School Administrators
Bibliographic record
Abstract
BACKGROUND: An estimated 1 in 5 adolescents have sustained a concussion in North America. Teachers and school administrators are responsible for implementing academic accommodations and other supports for optimal return to learn following a concussion. The primary objective of this study was to describe the prevalence and feasibility of providing academic accommodations to students following concussion from the perspectives of middle and high school teachers and school administrators. METHODS: A cross-sectional survey was administered to teachers and school administrators (grades 7-12) across Canada online via REDCap. Participants were recruited via word-of-mouth and social media sampling. Survey responses were analyzed descriptively using proportions. RESULTS: The survey was completed by 180 educators (138 teachers and 41 school administrators), of whom 86% had previously provided academic accommodations to students following concussion, and 96% agreed that students should have access to accommodations following concussion. Some accommodations (eg, breaks, extra time) were provided more often and were more feasible to provide than others (eg, no new learning, reduced bright light). Educators reported limited preparation time and limited school personnel support to assist students following concussion. IMPLICATIONS FOR SCHOOL HEALTH POLICY, PRACTICE, AND EQUITY: The most feasible accommodations should be prioritized, ensuring students are supported within the school environment. CONCLUSIONS: Teachers and school administrators confirmed the importance of providing accommodations to students following 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".