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Record W4390945050 · doi:10.5334/ijic.icic23178

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).

2023· article· en· W4390945050 on OpenAlexaffabout
Joe Pedulla, Jen Recknagel, Melissa Chang, Howard Abrhams

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsAging in placeFeelingGerontologyIntegrated careRetirement communitySociologyAgency (philosophy)Public relationsBlueprintPsychologyHealth careNursingMedicinePolitical scienceSocial psychologyEngineering

Abstract

fetched live from OpenAlex

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?

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.311
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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