Protecting the privacy of technology users who have cognitive disabilities: Identifying areas for improvement and targets for change
Bibliographic record
Abstract
INTRODUCTION: Information Technologies (IT) may serve assistive roles that facilitate the interaction of people living with cognitive disabilities (CD) within their environments. However, there are some notable concerns related to privacy threats associated with the use of IT. The purpose of this study was to examine how assistive technology developers may best adapt over time to develop their IT to be resilient against threats to privacy. We therefore focused on the following areas: (1) developers' knowledge and practices related to privacy protection; (2) challenges when applying recommended practices, and; (3) preferred channels to acquire knowledge. METHOD: We conducted semi-structured interviews with ten technology developers who are members of the AGE-WELL network undertaking research and development of assistive technologies to be used by people who have cognitive disabilities. We used an inductive-deductive method for the analysis of qualitative data to examine participant responses and generate themes related to the study goals. RESULTS: Principal themes that emerged from the data include practices specific to populations with CD, challenges to obtaining consent to use of information, and preferred channels to acquire knowledge. CONCLUSION: We identify areas of focus for developing a knowledge mobilization strategy to improve relevant policies and practices.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".