Perceptions of effective implementation of applied behaviour analysis (ABA) in schools
Bibliographic record
Abstract
Policy/Program Memorandum Number 140 outlines the requirements for Ontario school boards to incorporate principles of Applied Behaviour Analysis (ABA) into school programs for students with Autism Spectrum Disorder (ASD). With increasing numbers of children being diagnosed with ASD, it is important to assess the facilitators and barriers in implementing this policy, and work towards effective academic and social inclusion. Through individual interviews and follow-up questionnaires, four behaviour therapists provided their perceptions and experiences of ABA in the classroom. A thematic analysis yielded five main themes: reinforcement in the classroom, consistency, ABA and behaviours in the classroom, collaboration, and attributions. These themes are interpreted using a social model of disability and a children’s rights lens to answer the research question: what are the facilitators and barriers to the effective implementation of ABA in schools? Limitations, recommendations for future research, and practical recommendations are discussed. Key words: Applied behaviour analysis, PPM 140, autism spectrum disorder, inclusion, social model of disability
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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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".