Device abandonment in deafblindness: a scoping review of the intersection of functionality and usability through the International Classification of Functioning, Disability and Health lens
Bibliographic record
Abstract
OBJECTIVES: Abandonment of vision, hearing or mobility aids suggests common barriers and facilitators to ongoing device use. However, the possible interactive effects of combined hearing and vision disabilities on device use by those living with deafblindness are unclear. Here we summarise existing knowledge on variables influencing assistive technology use from the perspective of persons living with deafblindness. We used the WHO's International Classification of Functioning, Disability and Health (ICF) framework to contextualise the findings, asking 'What is currently known about variables influencing the (non-)use of assistive devices recommended for persons with deafblindness?' DESIGN: A scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews Checklist. DATA SOURCES: PubMed; ProQuest: ERIC; ProQuest Dissertation; ProQuest: Sociological Thesaurus; Web of Science; Scientific Electronic Library Online; Bielefeld Academic Search Engine; Pascal & Francis; APA PsycINFO and Ebsco for CINAHL were searched through 9 November 2020. ELIGIBILITY CRITERIA: We included peer-reviewed studies that reported on assistive technology, device abandonment/utilisation and provided data from persons living with deafblindness. DATA EXTRACTION AND SYNTHESIS: Four team members independently scored 83 studies for eligibility. RESULTS: Ten articles were chosen for data extraction. The emerging variables replicated established categories of barriers and facilitators: personal, device-related, environmental and intervention variables. The use of the ICF highlighted how an intermediate variable (eg, device acceptability) was necessary in order for a variable to become a barrier or a facilitator to device use. CONCLUSIONS: The variables influencing device use by persons with deafblindness followed the same categories described for single impairments. Usability was challenged in devices that rely on the 'other' sense. Haptic and tactile aids are rarely studied. The limited available information and the dire need for assistive technologies for people with deafblindness emphasises the urgency of research and technology development for this marginalised population.
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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 | 0.037 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.038 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".