The identification of assistive technologies being used to support the daily occupations of community-dwelling older adults with dementia: a cross-sectional pilot study
Bibliographic record
Abstract
Purpose: Assistive technologies (ATs) have tremendous potential to support occupations (i.e. meaningful daily activities) impacted by changes in cognition caused by dementia. However, little is known about what or how ATs are in use in community settings. This research created and piloted guided interviews intended to capture what ATs are in use, factors that affect use and gaps in support from multiple stakeholders. Method: Family caregivers (n = 3) and occupational therapists (n = 10) were chosen as pilot respondents because of their relationship to care provision, understanding of how occupations are impacted by changes in cognition and role in AT procurement. Data were analyzed using descriptive statistics. Results: The interviews' structures enabled data to be grouped into distinct categories and organized easily. The data illustrated the types of analysis that could be done given a larger sample size. It appeared that interviews captured ATs that were in use, as well as areas of non-use and perceived difficulties. Respondents identified several unmet needs and provided suggestions for desired outcomes. Conclusions: While the interview guides must be refined and validated, they are able to capture rich and comprehensive data that could be used by multiple stakeholders, such as clinicians, engineers and caregiver education groups, to target AT development, procurement, education and policy. Implications for RehabilitationStructured interview guide developed and piloted that could be used to identify ATs in use in the community to support older adults with dementia from the viewpoints of multiple stakeholders.These data could be used to: gain an understanding of AT use and non-use, discern differences in perception between the various stakeholders, and guide development, procurement, education and policy efforts.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".