EXISTING KNOWLEDGE ASSOCIATED WITH SMART HOME HEALTH TECHNOLOGIES IN THE CARE OF OLDER PERSONS: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Abstract As a greater part of the global population reaches the golden age, smart home technologies are said to allow older persons to remain independent at a place of residence, or “home”, of their own choice. Though their development has been making their way to the market, there has not been a systematic review of the empirical literature on the knowledge associated with their use for persons who are 65 years or older. Hence, we conducted a systematic review of empirical peer-reviewed English, German, and French articles in ten electronic databases. Data was textually described, separated into key characteristics, logged into a customized data extraction document, and analysed using narrative synthesis. The search across ten databases revealed 144 empirical papers that were admissible to our inclusion criteria. Of which, we discovered 5 first-order categories of benefits and 5 of barriers of smart home health technologies with further sub-themes that together form the concurrent array of existing knowledge. These categories included, for example, allows older persons to live independently at home, reminds older persons to promote self-care, and alternatively, concerns about usability, cost, and social acceptance. These systematically-derived categories of benefits and barriers could be a starting point for researchers interested in caregiving for older persons to conduct further empirical and reflective research. Furthermore, having this understanding of existing challenges and opportunities associated with smart home health technologies then allows the research and technical communities to collaborate upon a joint foundation to inform policy and improve caregiving for the global aging population.
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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 | 0.019 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".