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Record W3000465360 · doi:10.1111/jppi.12327

Functional Behavior Assessment Practices Used by Canadian Behavioral Health Practitioners

2020· article· en· W3000465360 on OpenAlexaffabout
Valdeep Saini, Alison D. Cox

Bibliographic record

VenueJournal of Policy and Practice in Intellectual Disabilities · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsBrock University
Fundersnot available
KeywordsAutism spectrum disorderPsychological interventionPsychologyDenialIntellectual disabilityAggressionClinical psychologyAutismGerontologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.137
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.137
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.180
GPT teacher head0.453
Teacher spread0.273 · 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 designQualitative
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

Citations12
Published2020
Admission routes2
Has abstractyes

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