Effectiveness of smart living environments to support older adults to age in place in their community: an umbrella review protocol
Bibliographic record
Abstract
INTRODUCTION: Frailty is a vulnerable condition exposing older adults to incidental adverse health events that negatively impact their quality of life and increase health and social costs. Digital solutions may play a key role in addressing this global problem and in particular, smart living environments. Smart living environments involve a notion of measurement or collection of data via several sensors, capturing the person's behaviours in the home or the person's health status over a long period of time. It thus has great potential for home support for older adults. The objective of this umbrella review will be: (1) to document the effectiveness of smart living environments to support ageing in place for frail older adults and (2) among the reviews assessing the effectiveness of smart living environment, to gather evidence on what factors and strategies were identified as influencing the implementation process. METHODS AND ANALYSIS: We will include systematic and scoping reviews of both quantitative and qualitative primary studies with or without meta-analysis focusing on assessing the effectiveness of interventions through smart living environments to support older adults in the community to age in place. The literature search will be done through the following biomedical, technological and sociological citation databases: MEDLINE, Embase, CINAHL, Web of Science and PsycINFO, and quality assessment of the reviews will be done thought AMSTAR2 checklist. The analysis of the results will be presented in narrative form. ETHICS AND DISSEMINATION: Our review will rely exclusively on published data from secondary sources and will thus not involve any interactions with human subjects. The results will be presented at international conferences and publications. PROSPERO REGISTRATION NUMBER: CRD42021249849.
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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 | 0.094 | 0.090 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.016 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".