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Record W3208396378 · doi:10.32920/ryerson.14653326.v1

Perceptions of effective implementation of applied behaviour analysis (ABA) in schools

2021· preprint· en· W3208396378 on OpenAlexaffabout
Heather Cowan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsWilfrid Laurier UniversityToronto Metropolitan UniversityEducation and Early Childhood DevelopmentGeorge Brown College
Fundersnot available
KeywordsThematic analysisInclusion (mineral)Autism spectrum disorderMemorandumPsychologyConsistency (knowledge bases)PerceptionAutismAttributionMedical educationQualitative researchDevelopmental psychologySocial psychologyMedicinePolitical scienceSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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.

Opus teacher head0.098
GPT teacher head0.422
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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