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Behavioural intervention practices for stereotypic and repetitive behaviour in individuals with autism spectrum disorder: a systematic review

2010· review· en· W2112607900 on OpenAlexaff
Stephanie Patterson, Veronica Smith, Michaela Jelen

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2010
Typereview
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAutism spectrum disorderIntervention (counseling)PsychologyAutismSystematic reviewClinical psychologyDevelopmental psychologyPsychiatryPsychotherapistMEDLINEBiology

Abstract

fetched live from OpenAlex

AIM: The purpose of this systematic review was to examine the quality of conduct of experimental studies contributing to our empirical understanding of function-based behavioural interventions for stereotypic and repetitive behaviours (SRBs) in individuals with autism spectrum disorders (ASDs). METHOD: Systematic review methodology was used to identify relevant articles, to rate the level of evidence and quality of conduct of the studies, and to extract data systematically. RESULTS: Ten single case studies examining 17 participants (14 males, 3 females; age 2y 11mo-26y) diagnosed with various ASDs were included. Overall, studies reported decreases in SRBs using behavioural interventions and some collateral increase in desirable behaviours. INTERPRETATION: Only a small number of intervention studies for SRBs explicitly state the function of the behaviour; therefore, relatively little is known about the efficacy of SRB interventions in relation to the range of possible behavioural functions. Evidence supporting SRB interventions is preliminary in nature, and caution should be used in choosing and implementing SRB intervention practices for individuals with ASDs.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.358
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations56
Published2010
Admission routes1
Has abstractyes

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