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Record W4409337919 · doi:10.5334/ijic.9459

Fostering Sustainable Aging in Place through Community Engagement and Older Adult Leadership: Lessons from the NORC Ambassador Program

2025· article· en· W4409337919 on OpenAlexaboutno aff
Jen Recknagel, Melissa Chang

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
Fundersnot available
KeywordsAging in placeGerontologyAged careAssisted livingCommunity engagementLong-term careRetirement communitySociologyPublic relationsMedicineNursingPolitical science

Abstract

fetched live from OpenAlex

Addressing the increasing desire of older adults to age in place and the limitations of traditional health and social care, the UHN NORC Program seeks to transform Naturally Occurring Retirement Communities (NORCs) into vibrant, self-supported communities through catalyzing resident volunteers, providing mentorship and coaching, and increasing access to the right service providers. This initiative aligns with the conference theme of ""Engaging Communities,"" as it reflects a grassroots movement for change led from within the community, incorporating local knowledge and lived experience, promoting self-management, and empowering people to advocate for their own health needs. The NORC Program from the University Health Network in Toronto, Canada, primarily targets older adults residing in NORC buildings, defined as regular residential high-rise buildings with a concentration of 30% or more older adults (65+). Program facilitators work alongside resident ‘Ambassadors’, building their capacity to take an active role in developing and sustaining a mutually supportive, aging in place community in their own building. Ambassadors undergo training in relational care, community development and participatory design and are supported through monthly meetings that follow a structured curriculum, emphasizing starting, doing, and sustaining community initiatives. The program connects NORC Ambassadors with local service providers who can deliver in-building social, health, and wellness programming based on resident interests and needs. Post-program, Ambassadors join an alumni network for continued support and learning. The program emphasizes resilience, flexibility, and community support, showcasing the importance of continuous learning and adaptation alongside older adults and community leaders. The program's success criteria include (1) building and strengthening informal support networks for aging in place, (2) creating social connections and reducing loneliness, (3) developing local older adult leadership and enhancing self-determination over activities, and (4) instilling a sense of ownership among older adults for aging in place initiatives. Personal and public involvement (PPI) is integral, with older adults actively participating in the design and implementation of NORC programming in their own communities and playing an essential role on the program advisory committee. This paper explores the opportunity NORC Programs present, showcasing their potential to transform regular high-rise buildings into connected, supportive environments for aging in place and facilitating continued care closer to home. It delves into the successes and challenges of implementation, highlighting positive cultural shifts, increased community engagement, and improved well-being among participants. Challenges include reaching frailer, isolated residents, potential Ambassador burnout, and the need for clearer engagement strategies for younger generations. The UHN NORC Program has been iterating and maturing its curriculum since 2019 and with more than 25 Ambassador groups representing ~4,000 older adults in Toronto, Canada’s most diverse urban centre. With representation from across the city, including different cultures and income levels, the Program is a model for fostering sustainable aging in place through community engagement and offers a roadmap for how to shift power to people and communities to create lasting, positive change. The paper invites international collaboration and knowledge-sharing to refine and expand this innovative approach, contributing to advancing integrated care and community engagement globally.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0050.002
Scholarly communication0.0030.002
Open science0.0030.006
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.136
GPT teacher head0.437
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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