Designing with Older Adults: How University Health Network's NORC Innovation Centre creating an integrated health and social care community for seniors residing in naturally occurring retirement communities (NORC).
Bibliographic record
Abstract
It is well known that, with adequate supports, many seniors desire to age in place in their own homes. NIC’s data shows that in Toronto, 70,000 seniors are living in 495 NORCs with over 53% of these having 2 or more co-morbid conditions. Rising to this challenge, the NIC's vision is to implement a 21st-century model of integrated health and social care in NORC buildings by developing health, social, and digitally-enabled solutions that provide Canadians with new options for aging in place with dignity and choice. Phase one, created and refined the NORC Ambassadors program (norcambassadors.ca) – an aging-in-place model based on mutual support, community engagement, and seniors' leadership. It was founded on a multi-year exploration that incorporated senior input, ethnographic observation, documentary stories, literature reviews, journey mapping, and the co-creation of service blueprints. Seniors led the implementation of the Ambassadors program based on participatory decision-making, self-management, agency, and choice. From the 2021 final report, 100% of respondents indicated a desire to continue organizing aging-in-place activities with 78% feeling their awareness of aging-in-place issues improved. Interestingly, 75% reported challenges with improving overall building engagement. Based on learnings from phase 1 and guided by IFIC's 9 pillars of integrated care, phase 2 layered in a service design approach to creating an enhanced model of health and social care that increases access to place-based services and support for seniors living in Toronto’s high-rise communities. Phase 2 involves over 100 Senior Advisors, 37 Specialists, national partners, and a growing array of system partners. Central to phase 2 work is developing the NIC's Integrated Health and Social Care model. Inviting senior advisors to lead co-design activities ensured that their voice is front and centre in a system for seniors by seniors. Leveraging multi-sector involvement supported the development of one team to enable the provision of services most important to seniors that span the entire continuum of care and determinants of health. NIC's model focuses on two parts of a person's journey – ""I want to stay healthy"" where people can access an array of services that help them stay healthy, and connected, and address social isolation and loneliness. This approach added the introduction of a NORC Animator to the Ambassador model from phase 1. NORC Animators, are on site and function to create relationships with the residents, coordinate group health and social activities, and are a friendly resource for all resident needs. In addition, the NORC Animator can also watch for functional change and; where seniors state that ""I want to get healthier""; support the connection to one-on-one health and social services. This presentation will address the following questions: 1) How does the NIC model compare with other existing models in Ontario 2) What is required to effectively support seniors in participatory design? And how can you sustain involvement? 3) What is most important in designing what support is needed and how it is delivered? 4) What lessons learned have been identified from the early adopter site experiences?
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".