Developing Smart Technology for Dementia Care in Transitional Care Units: Barriers to Participant Engagement & Alternative Strategies
Bibliographic record
Abstract
With an aging population, anticipated increase in dementia cases and the emergence of transitional care units (TCU) serving these individuals, healthcare providers are looking to the future of smart technologies (DementiaTech) to assist in providing care to persons living with dementia (PLWD). Our research aimed to understand how DementiaTech can be effectively implemented and evaluated in a hospital transitional care setting to enhance the quality of life of dementia patients. This paper outlines the challenges we faced in implementing methods to suit the nuances of this context, recovery strategies to adapt the study to probe the research objective, and the results of our adaptations. The study had 2 phases: 1) analyze nighttime workflows in a TCU to implement a sensor-based motion monitoring system that can track patient activity overnight to enhance staff situation awareness (SA) of risk factors related to care; 2) develop a mobile application (app) based on information from patient’s caregivers/family members what they feel is important to know about the daily activity of the patient to involve them in care. During Phase 2, recruiting participants in TCUs presented multifaceted challenges such as staff member turnover, lost participants due to the linear approach of the study and the long lead times with recruitment over time. In response, our team developed alternative methods to explore the research objective which included involving subject matter experts from the steering committee and TCU management to provide their perspectives to develop a preliminary understanding from a clinical perspective. Our results highlight the insights gained from these ‘alternative’ participant groups, followed by a discussion of lessons learned to address engagement challenges. Despite recruitment difficulties, the study provides preliminary insights on developing apps to support caregivers/family members in staying connected to patients in TCUs.
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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 | 0.071 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".