MULTI-CASE STUDY OF A NOVEL HEALTHCARE DELIVERY MODEL TO SUPPORT AGING IN PLACE IN RURAL, REMOTE, AND SMALL URBAN AREAS
Bibliographic record
Abstract
Abstract Aging in place has long been a policy objective in Canadian healthcare, with accompanying concerns about older adults in rural and remote regions, and growing interest in small urban areas as distinct from large urban ones. As one of the six health authorities in British Columbia (BC), Interior Health (IH) developed and implemented an innovative healthcare delivery model to support aging in place for older adults, while addressing system issues of cost containment and resource allocation. By transferring funds from acute care to primary care, IH created Seniors Health and Wellness Centres (SHWCs) in two small urban areas (Kelowna and Kamloops) and a third one with two rural sites (Salmon Arm and Revelstoke). Our multi-case study aimed to compare how the SHWCs are meeting their objectives, and addressing the priorities of rural and small urban older adults and their social determinants of health (SDoH). We used mixed methods of data collection and analysis, including key informant interviews (n=9), service user questionnaires (n=10), document analysis (n=19), and secondary data analysis of service usage (n=2343) to answer research questions (RQs) on the outcomes and impacts of IH’s restructuring. The results show the outcomes (RQ1) to be three SHWCs which vary considerably in their design and usage, including access, quality, and continuity of care. Our findings on the impacts (RQ2) indicate the SHWCs are meeting their cost reduction aims while concentrating on some SDoH (income & dis/ability) and neglecting others (gender, racism, rurality), and progressing slowly in addressing certain priorities of older adults.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".