Factors affecting information technology use from the perspective of aging persons with cognitive disabilities: A scoping review of qualitative research
Bibliographic record
Abstract
BACKGROUND: Although aging persons with cognitive disabilities may benefit from information technologies (IT), researchers have identified barriers affecting their IT use. However, most studies do not emphasize the needs and experiences reported by these users themselves. OBJECTIVE: To identify factors affecting IT use from the perspective of aging persons with cognitive disabilities. METHODS: We conducted a scoping review of peer-reviewed studies published between January 2008 and December 2018 that investigated IT use by aging persons with cognitive disabilities as reported by these individuals. Factors affecting participants’ IT use were synthesized through a thematic analysis of relevant studies’ findings. RESULTS: Seven studies were included in our analysis. We found technology-related (accessibility, usefulness, cost), social (support, stigma and other social pressure), and personal (experience with IT, attitudes toward IT use, functional limitations, life situation) factors related to participants’ IT use. Stigma was identified as a key barrier to IT use that has been underestimated in previous quantitative research. CONCLUSIONS: Understanding the role that stigma plays in the use and adoption of technology among aging persons with cognitive disabilities is critical to developing successful strategies to promote this population’s IT use.
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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 | 0.033 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".