Approaches to Understanding the Impact of Technologies for Aging in Place: A Mini-Review
Bibliographic record
Abstract
BACKGROUND: There are many approaches to evaluating aging-in-place technologies. While there are standard measures for outcomes such as health and caregiver burden, which lend themselves to statistical analysis, researchers have a harder time identifying why a particular information and communication technology (ICT) intervention worked (or not). OBJECTIVE: The purpose of this paper is to review a variety of methods that can help answer these deeper questions of when people will utilize an ICT for aging in place, how they use it, and most importantly why. This review is sensitive to the special context of aging in place, which necessitates an evaluation that can explore the nuances of the experiences of older adults and their caregivers with the technology in order to fully understand the potential impact of ICTs to support aging in place. METHODS: The authors searched both health (PubMed) and technology (ACM Digital Library) venues, reviewing 115 relevant papers that had an emphasis on understanding the use of aging-in-place technologies. This mini-review highlights a number of popular methods used in both the health and technology fields, including qualitative methods (e.g. interviews, focus groups, contextual observations, diaries, and cultural probes) and quantitative methods (e.g. surveys, the experience sampling method, and technology logs). RESULTS: This review highlights that a single evaluation method often is not adequate for understanding why people adopt ICTs for aging in place. The review ends with two examples of multifaceted evaluations attempting to get at these deeper issues. CONCLUSION: There is no proscriptive formula for evaluating the intricate nuances of technology acceptance and use in the aging-in-place context. Researchers should carefully examine a wide range of evaluation techniques to select those that will provide the richest insights for their particular project.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".