A Technology System to Help People With Intellectual Disability and Blindness Find Room Destinations During Indoor Traveling: Case Series Study
Bibliographic record
Abstract
BACKGROUND: People with severe or profound intellectual disability and visual impairment tend to have serious problems in orientation and mobility and need assistance for their indoor traveling. The use of technology solutions may be critically important to help them curb those problems and achieve a level of independence. OBJECTIVE: This study aimed to assess a new technology system to help people with severe to profound intellectual disability and blindness find room destinations during indoor traveling. METHODS: A total of 7 adults were included in the study. The technology system entailed a barcode reader, a series of barcodes marking the room entrances, a smartphone, and a special app that controlled the presentation of different messages (instructions) for the participants. The messages varied depending on whether the participants were (1) in an area between room entrances, (2) in correspondence with a room entrance to bypass, or (3) in correspondence with a room entrance representing the destination to enter. The intervention with the technology system was implemented according to a nonconcurrent multiple baseline design across participants. Sessions included 7 traveling trials, in each of which the participants were to reach and enter a specific room (1 of the 7 or 9 available) to deliver an object they had carried (transported) during their traveling. RESULTS: The participants' mean frequency of traveling trials completed correctly was between zero and 2 per session during the baseline (without the system). Their mean frequency increased to between about 6 and nearly 7 per session during the intervention (with the system). CONCLUSIONS: The findings suggest that the new technology system might be a useful support tool for people with severe to profound intellectual disability and blindness.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".