“Being Integrated Does Not Mean Being Included”: What Factors Contribute to School Exclusion for Autistic Children?
Bibliographic record
Abstract
Autistic students face a heightened risk of exclusion from school and related activities, yet the factors contributing to this issue remain poorly understood. To address this gap, the current study took place in Ontario, Canada's largest province, where diverse populations and varied inclusive education policies create unique challenges. The study had two primary objectives: (1) to examine the relationship between parent satisfaction with the individual education plan (IEP) process and school exclusion, and (2) to identify key factors parents perceive as predictors of school exclusion in their autistic children. A total of 412 caregivers from Ontario completed an online survey, available in English and French, between April and July 2018. Quantitative analysis revealed that greater satisfaction with the IEP process was associated with a lower likelihood of school exclusion (b = -0.297, OR = 0.743, p < 0.001). Qualitative analysis of open-ended responses identified two primary contributors to exclusion: bullying by peers and inadequate training and support for school staff. These findings highlight the need for improved supports in educational settings, including comprehensive anti-bullying initiatives, stronger collaboration with parents in the development of IEPs, greater accountability in ensuring that IEPs are properly implemented, a more inclusive approach to meeting student needs, and increased funding for support staff. Addressing these areas could help reduce the risk of exclusion and foster a more equitable learning environment for autistic students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".