Autism and the experiences of immigrant and culturally diverse families accessing behavioral interventions
Bibliographic record
Abstract
The current study investigates the cultural appropriateness of the delivery of Applied Behavior Analysis (ABA) to immigrant and culturally diverse families of children with autism. Due to the tensions that culture and value differences could generate, there is a need to question the cultural appropriateness ABA delivery to culturally diverse families of children with autism, whose worldviews and values are relatively different from Western values and cultural beliefs. The aim of the study is to contribute to a developing knowledge base on how ABA can be delivered effectively to immigrant and culturally diverse families of children with autism. Five families were recruited to participate in a semi-structured interview. Although participants prioritized their child’s educational and developmental needs over their socio-cultural needs, findings from the study revealed that ABA interventions could be adapted to meet the peculiar needs of culturally diverse families. The study also revealed some systemic barriers to successful parental engagement in ABA interventions, including delayed diagnosis, limited program capacity, age restrictions and complicated referral processes. Recommendations were made towards improving the experience of ABA interventions for the target population including parental capacity building, expansion of service alternatives, resources, and support for families.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".