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Record W4200263632 · doi:10.1002/bin.1857

Concurrent validity of Open‐Ended Functional Assessment Interviews with functional analysis

2021· article· en· W4200263632 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBehavioral Interventions · 2021
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsInstitut universitaire en santé mentale de MontréalInstitut Universitaire en Santé Mentale de QuébecUniversité de Montréal
FundersFonds de Recherche du Québec - Santé
KeywordsFunctional analysisPsychologyConcurrent validityFunctional approachFunctional theoryFunctional impairmentClinical psychologySocial psychologyPsychometricsDensity functional theory

Abstract

fetched live from OpenAlex

Abstract Open‐Ended Functional Assessment Interviews have limited empirical support for their concurrent validity with functional analysis. To address this issue, we conducted a study wherein 176 independent behavior analysts relied on data collected using Open‐Ended Functional Assessment Interviews to identify the function of challenging behavior in four children with autism. Then, we compared the results of their analyses with those of a traditional functional analysis. Our results showed that the conclusions drawn by behavior analysts using the Open‐Ended Functional Assessment Interviews corresponded with the outcomes of functional analyses in 74% of cases. These findings suggest that the Open‐Ended Functional Assessment Interview may inform functional analyses to develop initial hypotheses.

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.

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 score0.135
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0560.000

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 head0.716
GPT teacher head0.525
Teacher spread0.191 · 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