Best Practices for Living Labs When Studying Older Adults Living in Rural Communities
Bibliographic record
Abstract
Abstract There are two core concepts that make living labs distinguishable: involvement of users as co-creators and evaluation in a real-world setting. Living labs increase the potential for product acceptance and adoption due to testing and tailoring with target users. Currently, there is a lack of a universally accepted guideline for best practices. The objective of this review is to identify the best practices of living labs that can be recognized by the scientific community and followed in future labs. A 5-stage scoping review, following Arksey and O’Malley’s (2005) framework, was used to map out the coverage of different aspects of living lab methodology. A systematic search for articles involving living lab framework and older adults published between 2016-2021, was conducted in seven databases. Nine articles were included after review, the majority of which were published in health journals and were from Italy and the United States. An overview of consistent user involvement in the innovation process, real-world testing vs. laboratory testing, and participant scope findings will be shared. Multiple rounds of user feedback, real-world testing, and a small but diverse participant group were the most successful in increasing positive sentiments about the products tested in a living lab environment. The lack of published articles on living lab frameworks studying older adults indicate a gap in the literature. Creating a universally accepted definition for living labs and guidelines for best practices will allow for scientific validity and comparisons of studies and may increase the use and popularity of living labs.
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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 | 0.104 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.006 |
| 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".