Understanding the Needs of Ontario Educators in Supporting Students With Acquired Brain Injury in the Classroom
Bibliographic record
Abstract
BACKGROUND: When a child sustains an acquired brain injury (ABI), the impact extends to significant environments in their life, including school. Educator knowledge of ABI can influence a child's success with academic and social reintegration. An assessment of educator ABI knowledge was conducted to determine what information they require to support school reintegration. METHODS: A mixed-methods approach included a sampling of educators in a needs assessment survey and workshop. The survey determined levels of educator knowledge regarding ABI in the classroom, and the workshop scoped educator views in the development of a user-driven ABI learning program to enrich their expertise. RESULTS: Our sample reported being somewhat knowledgeable about ABI and the impact on students. There were no differences based on respondents' educational role. Teachers reported having minimal and inadequate supports for students following ABI during school transitions, feeling unprepared to assist students during these transitions, and that families also appeared unprepared for school reintegration following ABI. The workshop identified the need for a 2-part educational course. CONCLUSIONS: Supportive school environments are essential for the reintegration of students following ABI. This study identified educators' needs for ABI knowledge and resources to support their existing expertise.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".