Inclusion of autistic students: promising modalities for supporting a school team
Bibliographic record
Abstract
PURPOSE: School staff who work with autistic students are at a high risk of exhaustion. More training and guidance are needed to enable them to respond to these students' needs. The purpose of this study was to design and evaluate a professional development program offered by an occupational therapist to help a team of school staff facilitate the participation of autistic students and their peers. METHODS: A design-based research approach guided the development and evaluation of the modalities of the program. A case study was conducted in a school with nine school staff members using individual interviews, questionnaires, and a logbook. RESULTS: The program comprised five modalities: regular presence of the occupational therapist, a needs analysis, content focused on the features of an inclusive school, individual coaching, and team coaching. The following elements emerged as particularly helpful for the participants: frequent interactions with the occupational therapist, opportunities to collaborate, personalized support, experimentation of new practices with the occupational therapist, and concrete training content that can benefit all students. CONCLUSION: The modalities of the program appear promising to help a school team facilitate the participation of all students, including those who are autistic.Implications for rehabilitationOccupational therapists can help school teams to facilitate the participation of autistic students and their peers.A combination of individual and team coaching sessions appears promising in helping school staff adopt practice changes to make their school more inclusive.Using a multi-tiered approach can help therapists collaborate with school staff members to first find solutions that will be applicable with all of their students.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".