MétaCan
Menu
Back to cohort
Record W2281634094 · doi:10.1177/008124630103100105

The Tomatis Method with Severely Autistic Boys: Individual Case Studies of Behavioral Changes

2001· article· en· W2281634094 on OpenAlexaff
Joan M. Neysmith-Roy

Bibliographic record

VenueSouth African Journal of Psychology · 2001
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsAutismPsychologyActive listeningDevelopmental psychologyChildhood Autism Rating ScaleRating scaleClinical psychologyAutism spectrum disorderPsychotherapist

Abstract

fetched live from OpenAlex

Six severely autistic males ranging in age from 4 years to 11 years received the Tomatis Method to assist in alleviating the severity of behaviours contributing to the diagnosis of autism. Ten minute video samples were taken of each boy, under two conditions of play, every time he completed one section of the treatment programme. As measured by the Children's Autism Rating Scale (CARS) all of the boys were severely autistic at the beginning of treatment. Three (50%) of the boys demonstrated positive behavioural changes by the end of the treatment. One boy was no longer considered to be autistic, two boys showed mild symptoms of autism and three boys remained within the severely autistic range. Of particular interest were the changes that occurred in pre-linguistic areas for five of the six boys. These included Adaptation to Change, Listening Response, Non Verbal Communication, Emotional Response and Activity Level. These behaviours are considered prerequisites for successful verbal communication. The children who demonstrated behavioural change were 6 years of age or younger at the beginning of treatment. The author suggests that the Tomatis Method may be helpful in making prelinguistic behaviours manageable and thus help prepare the child to learn basic skills necessary for the development of language and learning.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.142
GPT teacher head0.438
Teacher spread0.296 · 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 designCase report
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

Citations34
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueSouth African Journal of PsychologySame topicAutism Spectrum Disorder ResearchFrench-language works237,207