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Record W4413358419 · doi:10.5334/ijic.nacic24123

Oasis Aging in Place: An Innovative National Model Supporting Healthy Aging - Lessons and Successes from Growth and Expansion

2025· article· en· W4413358419 on OpenAlexaboutno aff
Alisha Matte, Jennifer Wilkie, Vincent DePaul, Catherine Donnelly, Riley Malvern, Andrew Nguyen, Allen G. Prowse, Helen Cooper, Elaine Watier, Sarah Webster, Carri Hand, Debbie Laliberté Rudman, Lori Letts, Julie Richardson

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAging in placeGerontologySuccessful agingProcess managementIntegrated careMedicinePsychologyEngineeringHealth careEconomic growthEconomics

Abstract

fetched live from OpenAlex

Background: Over the next 20 years, Canada older adult population (aged 65 years old) is expected to grow by 68%. Older adults face isolation, loneliness, inadequate nutrition, and lack of physical activity which continue to be serious and growing threats to aging well in place. Addressing these challenges requires multifaceted approach involving service providers, community organizations, and people. To meet this need, an innovative model known as Oasis; was developed. Approach: Developed as an innovative solution to support aging well at home, the first Oasis Program was co-created with a group of older adults living in an apartment building in Kingston Ontario in 20. Since then, through a University-community partnership Oasis has expanded from one original site in Kingston, Ontario to 9 communities across Canada. Oasis works in collaboration with public sector, not-for-profit, and private sector organizations to develop a supportive living program for older adults that builds community among members in Naturally Occurring Retirement Communities (NORCs), these often being apartment buildings and other multi-resident settings (e.g. mobile home park, neighbourhood).The Oasis model is member-driven and designed to enhance the well-being and social engagement of older adults. It provides a supportive environment where individuals can co-create and participate in a variety of physical, social and nutrition-based activities, programs, and services that promote healthy aging, lifelong learning, and social interaction. Membership in Oasis is entirely voluntary and free, allowing members to participate to the extent of their own choosing. An onsite Coordinator responds to member needs and interests by organizing and identify existing community programs to bring into Oasis or implements site specific programming. Programming may be delivered by the coordinator, older adult members, or volunteers. In many cases, community agencies bring programs or information sessions the members in the building or neighbourhood.An Oasis community requires a common space that can be used for programming and social engagement. For Oasis Buildings, this can be in an apartment building or a condo common room. Whereas for Oasis Neighborhoods this can be in a central community space (e.g., a church, school). Results: Extensive research and evaluations have proven Oasis to be a successful and impactful model.Older adults living in a community with Oasis report increased wellbeing, and a safe and inclusive community. On average, they experience lower rates of loneliness, falls, emergency visits, hospitalizations, home care service, and a delayed transition to long term care.It is also a cost-effective model. Through partnerships with landlords, free of charge spaces are provided for activities and programs. The programs of existing community health and social service agencies are leveraged to meet the needs of Oasis members. Funding for each Oasis sites supports the hiring of a program coordinator.Oasis also fosters cross-collaborative partnerships between public, private and non-profit sector. The program is partnered with a local service agency, called the community site partner which supply financial and budget management and human resource expertise. Implications: Oasis is now at a critical juncture. With a proven and impactful model, demand has grown significantly and there is opportunity for further expansion. This is matched, however, with the need to create formal operational and governance structures to achieve both consistency and sustainability. Next steps in the expansion of the Oasis model include developing a detailed plan to achieve sustainability, establish governance and corporate structures, and implement quality control for all existing and potential future Oasis sites. This process of growth and expansion has revealed many lessons learned, which we aim to share with others as we continue to evolve our model and operations.

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)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.249
Threshold uncertainty score0.496

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0100.006
Scholarly communication0.0070.005
Open science0.0030.016
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.003

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.

Opus teacher head0.028
GPT teacher head0.410
Teacher spread0.382 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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