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Record W4417501381 · doi:10.1080/26892618.2025.2602128

Aging Where the Road Ends: Navigating Health-Care and Housing – The Case of a Remote British Columbian Community

2025· article· en· W4417501381 on OpenAlexaffabout
Shawna Hopper, John Pickering, Andrew Wister

Bibliographic record

VenueJournal of Aging and Environment · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsFraser HealthSimon Fraser University
Fundersnot available
KeywordsWork (physics)IndigenousGovernment (linguistics)Ethnic group

Abstract

fetched live from OpenAlex

Canada’s rapidly aging population magnifies long-standing gaps in community, health, and housing infrastructures, especially in more rural and remote areas. To illuminate these challenges, we conducted a qualitative case study in Port Hardy, a geographically isolated town on northern Vancouver Island. Twenty-four in-depth interviews were conducted with community-dwelling adults aged 65–85 to explore experiences of health-care access, housing suitability, and community support. Reflexive thematic analysis revealed three compounding barriers: (1) fragile clinical capacity, after-hours emergency closures, rotating short-term physicians, and intermittent home-care and ambulance coverage; (2) an acute shortage of age-appropriate and transitional dwellings that traps older residents in unsuitable homes and deters incoming health professionals; and (3) reliance on informal neighbour-to-neighbour support that, while valued, cannot offset structural service gaps. Participants framed these issues as problems of remoteness rather than generic rurality, underscoring the unique contextual dimensions of Aging in the Right Place model. Findings call for integrated policy responses, coordinated investment in age-friendly housing, stable multi-year funding for rural health services, and transport solutions, to enable older adults to age better.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.340
Teacher spread0.320 · 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 designOther design
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 routes2
Has abstractyes

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