Determinants of Successful Implementation of Assistive Technologies for Dementia: Exploratory Survey
Bibliographic record
Abstract
Background: Despite positive results for the use of assistive technologies (ATs) in dementia, the uptake of ATs lags behind. It is considered important to assess determinants of successful or unsuccessful implementation of ATs. Objective: We explored factors that influence the implementation of ATs for community-dwelling people with dementia, with the aim to better understand potentially effective implementation strategies. Methods: A cross-sectional survey for researchers was developed and disseminated, exploring factors that influence either successful or unsuccessful implementation of ATs for dementia. The survey consisted of closed and open questions. Results: The response rate was 10% (21/206); the 21 respondents who completed the survey were from 8 countries. Determinants of implementation were described for 21 ATs, of which 12 were successfully and 9 were unsuccessfully implemented. Various types of ATs were included, such as online platforms, sensors, or physical aids. The main determinants of implementation success were related to the AT itself, contextual factors, research activities, and implementation strategies. There was a lack of research data on some ethical issues and cost-effectiveness. Conclusions: This study provided insight into some main barriers to and facilitators of implementation of ATs in dementia related to the AT itself, context, research-related activities, and applied implementation strategies. Lessons were formulated for various stakeholders to improve the implementation effectiveness of ATs in dementia.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".