A Technology System to Help People With Multiple Disabilities Increase Contact With Objects and Control Environmental Stimulation: Single-Case Research Design
Bibliographic record
Abstract
BACKGROUND: People with severe-to-profound intellectual disability and sensory-motor impairment tend to be passive and detached from their immediate context. OBJECTIVE: This study assessed a new technology system using a webcam to detect participants' responses (ie, hand contact with objects) and to trigger computer delivery of preferred environmental stimulation, such as music, contingent on (immediately after) the occurrence of those responses. METHODS: In total, 8 adults with severe to profound intellectual disability and extensive motor and visual impairments participated in the study. Each participant was exposed to an ABACB design. The technology system did not provide stimulation during the A (baseline) phases, provided stimulation contingent on the responses during the B (intervention) phases, and provided stimulation throughout the sessions during the C (control) phase. Sessions lasted 5 minutes. RESULTS: During the first baseline phase, the participants' mean frequency of responses per session was between about 3 and 6.5. During the first intervention phase, it increased to between about 10 and 18. It showed a clear decline during the second baseline phase, remained low during the control phase, and increased again during the second intervention phase. During this phase, it ranged from about 13 to 19.5. CONCLUSIONS: The new technology system might be a useful tool to help people with intellectual and sensory-motor disabilities increase object contact and stimulation control.
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 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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".