Factors influencing sustainability and scale-up of rural primary healthcare memory clinics: perspectives of clinic team members
Bibliographic record
Abstract
BACKGROUND: The aging of rural populations contributes to growing numbers of people with dementia in rural areas. Despite the key role of primary healthcare in rural settings there is limited research on effective models for dementia care, or evidence on sustaining and scaling them. The purpose of this study was to identify factors influencing sustainability and scale-up of rural primary care based memory clinics from the perspective of healthcare providers involved in their design and delivery. METHODS: Participants were members of four interdisciplinary rural memory clinic teams in the Canadian province of Saskatchewan. A qualitative cross-sectional and retrospective study design was conducted. Data were collected via 6 focus groups (n = 40) and 16 workgroup meetings held with teams over 1 year post-implementation (n = 100). An inductive thematic analysis was used to identify themes. RESULTS: Eleven themes were identified (five that influenced both sustainability and scale-up, three related to sustainability, and three related to scale-up), encompassing team, organizational, and intervention-based factors. Factors that influenced both sustainability and scale-up were positive outcomes for patients and families, access to well-developed clinic processes and tools, a confident clinic leader-champion, facilitation by local facilitators and the researchers, and organizational and leadership support. Study findings revealed the importance of particular factors in the rural context, including facilitation to support team activities, a proven ready-to-use model, continuity of team members, and mentoring. CONCLUSIONS: Interdisciplinary models of dementia care are feasible in rural settings if the right conditions and supports are maintained. Team-based factors were key to sustaining and scaling the innovation.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".