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Record W2055559036 · doi:10.1108/13595474200100012

Communication Partnerships with People with Profound and Multiple Learning Disabilities

2001· article· en· W2055559036 on OpenAlexaboutno aff
Jill Bradshaw

Bibliographic record

VenueTizard Learning Disability Review · 2001
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsLearning disabilityPsychologyGeneral partnershipDiversity (politics)Independence (probability theory)Psychological interventionPopulationDevelopmental psychologySocial psychologyCognitive psychologySociology

Abstract

fetched live from OpenAlex

There are many definitions of profound and multiple learning disabilities. Most definitions include having a high degree of learning disability in conjunction with at least one other severe impairment, such as visual, auditory or physical impairments (Male, 1996; Ware, 1996; Lacey, 1998). Bunning (1997) adds that people with such disabilities are very reliant on others for support, including support in taking part in communicative events. Establishing reliable and consistent methods of communication may be exceptionally difficult (Florian et al, 2000). However, it is important to consider the individuality and extreme diversity of this population (Detheridge, 1997; Hogg, 1998), which includes variability in communication strengths and needs (Granlund & Olsson, 1999; McLean et al, 1996). Communication is often given little attention when services are planning ways of supporting individuals to participate, develop independence and make choices (McGill et al, 2000). While the individual's communication strengths and needs should remain central within any discussion, the significant others and the environment will also have an important influence. This article explores some of the communication issues experienced by people with profound and multiple learning disabilities and highlights the importance of the communication partnership within interventions.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.116
GPT teacher head0.409
Teacher spread0.294 · 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 designObservational
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

Citations17
Published2001
Admission routes1
Has abstractyes

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