A five-step approach for developing and implementing a Rural Primary Health Care Model for Dementia: a community–academic partnership
Bibliographic record
Abstract
AIM: This study is aimed at developing a Rural Primary Health Care (PHC) Model for delivering comprehensive PHC for dementia in rural settings and addressing the gap in knowledge about disseminating and implementing evidence-based dementia care in a rural PHC context. BACKGROUND: Limited access to specialists and services in rural areas leads to increased responsibility for dementia diagnosis and management in PHC, yet a gap exists in evidence-based best practices for rural dementia care. METHODS: Elements of the Rural PHC Model for Dementia were based on seven principles of effective PHC for dementia identified from published research and organized into three domains: team-based care, decision support, and specialist-to-provider support. Since 2013 the researchers have collaborated with a rural PHC team in a community of 1000 people in the Canadian province of Saskatchewan to operationalize these elements in ways that were feasible in the local context. The five-step approach included: building relationships; conducting a problem analysis/needs assessment; identifying core and adaptable elements of a decision support tool embedded in the model and resolving applicability issues; implementing and adapting the intervention with local stakeholders; and sustaining the model while incrementally scaling up. RESULTS: Developing and sustaining relationships at regional and PHC team levels was critical. A comprehensive needs assessment identified challenges related to all domains of the Rural PHC Model. An existing decision support tool for dementia diagnosis and management was adapted and embedded in the team's electronic medical record. Strategies for operationalizing other model elements included integrating team-based care co-ordination into the decision support tool and family-centered case conferences. Research team specialists provided educational sessions on topics identified by the PHC team. This paper provides an example of a community-based process for adapting evidence-based practice principles to a real-world setting.
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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 | 0.076 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".