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Record W2269320933 · doi:10.1177/215416471505000409

An Evaluation of a Self-Instructional Manual for Teaching Individuals How to Administer the Revised ABLA Test

2015· article· en· W2269320933 on OpenAlexaff
Ashley Boris, Nardeen Awadalla, Toby L. Martin, Garry L. Martin, Lauren Kaminski, Morena Miljkovic

Bibliographic record

VenueEducation and training in autism and developmental disabilities · 2015
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychologyAutismIntellectual disabilityTest (biology)Developmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

The Assessment of Basic Learning Abilities (ABLA) is a tool that is used to assess the learning ability of individuals with intellectual disability (ID) and children with autism. The ABLA was recently revised and is now referred to as the ABLA-Revised (ABLA-R). A self-instructional manual was prepared to teach individuals how to administer the ABLA-R (DeWiele, Martin, Martin, Yu, & Thomson, 2011). Using a modified multiple-baseline design across a pair of university students, and replicated across four pairs, we evaluated the effectiveness of the ABLA-R self-instructional manual for teaching the students how to administer the ABLA-R. Each student: (a) after examining a brief description of the ABLA-R, attempted to administer the ABLA-R to a confederate role-playing an individual with ID (Baseline); (b) studied the ABLA-R selfinstructional manual (Training); and (c) re-attempted to administer the ABLA-R to a confederate (Post-Training Assessment). Participants who achieved at least 90% accuracy in conducting the ABLA-R in their Post-Training Assessment then administered the ABLA-R to an individual with ID in a Generalization Assessment. Although additional research is needed, our results suggest that the self-instructional manual is an effective tool for training individuals to accurately administer the ABLA-R.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.099
GPT teacher head0.381
Teacher spread0.281 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2015
Admission routes1
Has abstractyes

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