Using Cognitively Accessible Survey Software on a Tablet Computer to PromoteSelf-Determination among People with Intellectual and Developmental Disabilities
Bibliographic record
Abstract
People with intellectual and developmental disabilities (I/DD) identify “speaking for oneself” as a highly salient aspect of self-advocacy and self-determination (SABE, 2011), yet limitations in cognition or language often limit their direct participation in surveys. This study describes a self-administered survey procedure that used supportive software on an iPad to create a survey interface that was easily navigable by respondents with I/DD. A survey based on items from the National Core Indicator (NCI) Adult Consumer Survey (HSRI & NASDDDS, 2001) was developed that included five items on choice-making which have been previously studied by Lakin et al. (2008) and Stancliffe et al. (2011). Cognitively diverse groups of self-advocates were recruited to take the iPad survey at both a national and state self-advocacy conference. The results indicated that the iPad survey platform enabled people with varying degrees of I/DD to respond independently to a self-administered survey with little training or assistance. The resulting iPad-gathered data on the NCI choice items supported the validity of the procedure by conforming to patterns from standard NCI interviews. This self-administered survey technology holds great promise for gathering many types of survey information directly from people with I/DD, allowing them to more actively participate in the design of supports, services, and environments that affect their lives.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".