Functional Behavior Assessment Practices Used by Canadian Behavioral Health Practitioners
Bibliographic record
Abstract
Abstract A proportion of children and adults in Canada are identified as having an intellectual or developmental disability (I/DD). Moreover, the prevalence of autism spectrum disorder (ASD) in Canadian children has increased substantially over the past decade. Research has shown that these populations have a greater likelihood to engage in severe destructive behavior such as self‐injury and aggression, which places them at risk for exposure to intrusive interventions and denial of services. Functional behavior assessment (FBA) is an assessment strategy that takes the environmental variables responsible for the development and maintenance of destructive behavior into consideration and has shown to be the most informative for developing effective treatments for destructive behavior. We conducted a nation‐wide survey that queried behavior‐analyst practitioners working in the I/DD and ASD service sectors about their beliefs and use of FBA in clinical practice. We compared the results of this survey with similar surveys conducted in the United States. The results indicated that most Canadian practitioners are conducting some type of FBA; however, many of these assessments are not comprehensive and missing important components of the assessment process. We discuss the implications of these findings, as well as the barriers to implementing FBA in practice.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".