Prescribing Assistive Technology for Cognition to Support Aging in Place: OTs’ Perspective
Bibliographic record
Abstract
Background. With a rapidly aging population, ensuring the safety and independence of older adults, particularly those with cognitive impairments, is a key public health priority. Occupational therapists (OTs) play a crucial role by recommending assistive technologies for cognition (ATCs) to support this population. However, little is known about how OTs choose ATCs, and the rehabilitation strategies involved in their implementation. Purpose. This study examines OTs’ perspectives on prescribing ATCs to support aging in place, focusing on (1) factors influencing ATC recommendations and (2) effective rehabilitation strategies. Methods. We conducted a descriptive qualitative study with 15 geriatric-focused OTs across three focus groups. Discussions were analyzed through three steps: coding, refining, and creating data matrices. Findings. OT recommendations are influenced by client factors (e.g., learning ability), specific tasks (e.g., medication management), and contextual elements (e.g., financial support). OTs employ cognitive rehabilitation, practice simulations, and caregiver collaboration strategies to support ATC integration. Conclusions. Understanding how OTs choose and apply ATCs provides insights to optimize their use in geriatric care, promoting safer, independent living for older adults.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".