Applied Behaviour Analysis (ABA): A Transformative Mixed Methods Analysis of Experiences and Perspectives from Autistic People, Parents, and ABA Providers
Bibliographic record
Abstract
The following mixed methods dissertation examines experiences of Applied Behaviour Analysis (ABA) from the perspective of autistic people, parents, and ABA providers in Canada. Using survey and interview analysis, this study explores a growing divide in standpoints regarding ABA as a method of treating and supporting autistic young people. Autistic people across the globe have shared their lived experiences of the ways ABA has caused them harm, and often trauma, due to the rigidly applied reward and punishment strategies that were used to make them appear less autistic and more neurotypical. Yet, ABA remains the most recommended autism intervention in Ontario. To understand this problem more in-depth, the methodology adopts a Critical Autism Studies (CAS) framework, and transformative explanatory sequential mixed methods design, to seek autistic justice. The four-staged methodology (surveys – interviews – analysis – participatory dissemination with co-authorship of policy and practice directions with autistic participants) responds to Damian Milton’s (2014) call for “interactional expertise” between non-autistic researchers and autistic people to improve autism practice and policies through a strength-based understanding of autism. Eight participants with either positive or negative experiences with ABA (4 autistic people, 2 parents, and 2 ABA providers) were purposively sampled for interviews from a total of 100 survey respondents. Using a mix of quantitative analyses followed by open text box responses and eight “Stories Behind the Statistics”, the research aimed to explore the questions: What are the impacts of ABA services on autistic people’s well-being and quality of life (QoL)? Are ABA services meeting the needs of autistic people? As the research progressed, two new research questions emerged: In what ways do power and stigma influence experiences with ABA interventions? How can diverse autistic expertise transform autism intervention practices and policy? Findings did not reveal compelling evidence that ABA is an approach that improves the quality of life or wellbeing of autistic people. While some respondents shared positive experiences of ABA, findings revealed evidence of harm, which raises ethical concerns about the services received by autistic people in capitalist colonial societies. The dissertation concludes with recommendations co-authored with four autistic recipients of ABA.
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 | 0.043 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".