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Record W2137854333 · doi:10.1177/1744629514558075

The influence of staff training on challenging behaviour in individuals with intellectual disability

2014· review· en· W2137854333 on OpenAlexaff
Alison D. Cox, Charmayne Dubé, Beverley Temple

Bibliographic record

VenueJournal of Intellectual Disabilities · 2014
Typereview
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsSt.AmantUniversity of Manitoba
Fundersnot available
KeywordsPsychologyChallenging behaviourTraining (meteorology)Identification (biology)Inclusion (mineral)Intellectual disabilitySample (material)Medical educationApplied psychologySample size determinationBest practiceProfessional developmentClinical psychologyMedicinePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Many individuals with intellectual disability engage in challenging behaviour. This can significantly limit quality of life and also negatively impact caregivers (e.g., direct care staff, family caregivers and teachers). Fortunately, efficacious staff training may alleviate some negative side effects of client challenging behaviour. Currently, a systematic review of studies evaluating whether staff training influences client challenging behaviour has not been conducted. The purpose of this article was to identify emerging patterns, knowledge gaps and make recommendations for future research on this topic. The literature search resulted in a total of 19 studies that met our inclusion criteria. Articles were separated into four staff training categories. Studies varied across sample size, support staff involved in training, study design, training duration and data collection strategy. A small sample size (n = 19) and few replication studies, alongside several other procedural limitations prohibited the identification of a best practice training approach.

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)0.005
metaresearch head score (Gemma)0.068
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.845
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.068
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
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 head0.091
GPT teacher head0.377
Teacher spread0.286 · 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 designOther design
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

Citations28
Published2014
Admission routes1
Has abstractyes

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