A RAPID REALISTIC REVIEW TO INFORM AN IMPLEMENTATION PROJECT; ASSISTIVE TECHNOLOGIES WITH CO-DESIGN AND OLDER ADULTS
Bibliographic record
Abstract
Abstract Objective This presentation reports on a rapid realist review of participatory co-design approaches to developing assistive technologies with older adults. It provides an evidence synthesis of the key mechanisms, contexts and outcomes that drive success and failure in the use of participatory co-design in this field. This KISSS-AT sub-project, was undertaken to help inform stakeholder partners and the wider gerontechnological community of the key challenges and opportunities, that participatory co-design offers the field. Methods We conducted a rapid realist review (Saul et al., 2013). We identified 1060 citations from databases (AgeLine, BSC, CINAHL, MEDLINE, PycINFO, Sociological Abstracts and Web of Science), and 936 from hand journal searches (Ageing and Society, CSCW, Gerontechnology). We screened 311 full-text articles, with 19 articles for extraction. We extracted an additional 9 articles in order to capture post-search publications and a few articles identified through additional snowball citation searches. We analyzed the data for context-mechanism-outcome configurations (Pawson, 2015; Wong et al., 2013) that we found were relevant to our initial program theory, and used analytical induction to test emerging themes with our data set. Results We identified two key theoretical dimensions (1. Epistemological 2. Ethico-political) of participatory co-design with older adults, as part of our program theory, along with several C-M-O-Cs related to each dimension. Conclusions We found that paying attention to the underlying dimensions of participatory co-design, and the key mechanisms and contexts that support its successful implementation is fundamental to realizing the promise of this approach to gerontechnology development and implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".