Communication Partnerships with People with Profound and Multiple Learning Disabilities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".