Key informant perceptions of challenges and facilitators to implementing passive remote monitoring technology for home care clients
Bibliographic record
Abstract
Background: Passive remote monitoring technologies (RMT) are an option that could keep frail older adults home longer while reducing care burdens on family/friend caregivers. In contrast to active RMT which requires an individual to engage with the technology (i.e., push a button), passive RMT does not require any action to function (i.e., sensors or cameras). Objective: This qualitative study explored the challenges and facilitators of implementing passive RMT in home care settings by applying an implementation science lens. Method: Twenty semi-structured interviews were conducted with key informant stakeholders. Data were coded using a Framework Analysis approach that inductively and deductively coded transcripts. The analysis applied deductive codes based on the implementation science framework, the Consolidated Framework for Implementation Research (CFIR). Inductive coding ensured that the participants' perspectives were represented. Results: Although participants perceived passive RMT was beneficial, there were health system policies that made it hard for practitioners to share information on passive RMT with home care clients; thus, home care clients and their caregivers, who may not have the digital literacy to determine which RMT are suitable for the situation, were tasked with determining which RMT was suitable. Conclusion: Applying an implementation science lens helped identify what institutional barriers need to be addressed to integrate passive RMT into home care for older adults. The findings highlight the need to educate practitioners and policymakers on when passive RMT is appropriate for home care clients. Disseminating information on passive RMT to older adults and their families could increase their awareness and facilitate decision-making.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".