Mainstream Technologies in Facilities for People With Intellectual Disabilities: Multiple-Methods Study Using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability Framework
Bibliographic record
Abstract
BACKGROUND: People with intellectual disabilities in residential or outpatient facilities for people with disabilities run the risk of being digitally excluded by not having opportunities for taking advantage of digitalization possibilities. OBJECTIVE: We aimed to investigate how disability caregivers and managers describe barriers and facilitating factors to implement and adopt mainstream technology for people with intellectual disabilities in residential or outpatient facilities and how the competencies and capabilities of the caregivers are assessed in the process. METHODS: For this reason, we conducted a multiple-methods study applying the nonadoption, abandonment, scale-up, spread, and sustainability framework. RESULTS: As a result, we identified barriers and facilitators across the nonadoption, abandonment, scale-up, spread, and sustainability framework domains: (1) condition-people with intellectual disabilities are a diverse group, where the individual condition of the person and, for example, their communication skills were seen as a prerequisite for implementing mainstream technologies; (2) technology-the extent to which mainstream technology fits the individual needs and demands contributed to the implementation process; (3) value proposition-communication was seen as a life area where mainstream technology can add value; (4) adopters-the caregivers needed competencies and capabilities to accompany their care recipients' technology use; (5) organization-missing legal regulations and lack of personnel resources were described as barriers; (6) wider context-funding opportunities were seen as unclear in disability services as mainstream technologies could not be financed as participation benefits; (7) embedding and adaptation over time-the COVID-19 pandemic forced facilities to become digitalized to some extent. CONCLUSIONS: The disability services investigated were still in need of standardized procedures to promote the digital participation of their residents.
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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".