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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".