Facilitating students’ return to school following a concussion: Perspectives of Canadian teachers and school administrators
Bibliographic record
Abstract
An estimated 20 percent of adolescents in North America have sustained a concussion. Teachers and school administrators are responsible for supporting following concussion to return to school. The purpose of this study was to describe the perspectives of Canadian teachers and school administrators with supporting returning to school post-concussion. This qualitative study was guided by an interpretivist philosophy. Semi-structured interviews with grades 7–12 teachers (n = 13) and school administrators (n = 5) were coded inductively and analysed using reflexive thematic analysis. We organized the data into five themes: 1) Educator roles differ for administrators and teachers; 2) Students’ symptoms affect their learning; 3) Students should have access to academic accommodations; 4) Students benefit from social support, compassion, and empathy; and 5) Concussion education and management processes are lagging. In Canada, teachers and administrators have different roles when supporting students returning to school after a concussion and those roles influence their engagement with the students and their awareness of students’ needs. Lack of concussion education and concussion management processes at schools may affect how students are supported following a concussion. Our findings can inform the development and implementation of supports to facilitate return to school for students following a 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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.011 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".