Design principles for the development of health technology with and for older adults to enhance well-being and aging in place.
Bibliographic record
Abstract
Introduction: Discussion of newly developed design principles for the development of health technology with and for older adults to enhance well-being and aging in place. Who is it for? The networking session is aimed at a broad audience including policymakers, practitioners, clinicians, and others interested in learning and contributing. The overall aim is to identify how our research can inspire and benefit others and help add value in the future planning of inclusive digital health services for older adults served by both primary and secondary healthcare services with cross-disciplinary teams. The networking session aims in collaboration with the participants to discuss and develop new principles for the design of health technology-, and services for older adults who live with physical or mental impairments. During the workshop we will share knowledge and data from an EU research project between EU and Canada called SMILE and develop design guidelines to involve older adults in the design and implementation of health technology. The purpose is to inspire the participants and spread the word about inclusive service design, to reduce inequity in health, and to increase accessibility, by including those not normally heard in the development of new health technologies. Ultimately, the aim is to make sure that a broader population of older in the future will gain the full benefit from future technologies. Two specific themes are addressed in the session, inclusion and stratification of users using the READHY instrument and how the ‘Epital Care Model’ can inform horizontal and vertical infrastructure in the development of an ecosystem. Who are you involving and engaging with? In the EU project SMILE, a new digital health technology is being designed with older adults living with one or more chronic conditions, and with different levels of digital and health literacy. The co-creational technology design approach pursued in SMILE is iterative, and includes initial interviews with end-users, workshops with developers, three phases of design workshops with end-users, and questionnaires. What are you doing or propose to do? Please explain the initiative or intervention. Developing technology with older adults with one or more chronic conditions, based on their needs and preferences, to increase well-being and ageing in place. In this process focus has been on including a broad variety of people to avoid inequity in access to health services, including, older with frailty or impairments, who are not normally included in the co-creation process and or development of new health technology. What is the problem or question you want their help to solve? - Input on how to include and design with older adults with frailty or impairments. - Reflection on the two models for stratified inclusion and design for different needs to avoid inequity in the digital transformation. - Feedback on how the ECM framework can be enhanced to ensure the involvement of informal caregivers, what works and what is missing.
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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 | 0.045 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".