A scoping review of innovations that promote interprofessional collaboration (IPC) in primary care for older adults living with age-related chronic disease in rural areas
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: An aging population and associated multi-morbid chronic diseases (CDs) require comprehensive health care across multiple disciplines. Literature suggests interprofessional collaboration (IPC) in primary care is effective for CD models of care. However, IPC requires innovative implementation, particularly in rural and remote areas where access to health care services and providers is often limited. Our main objective was to identify and synthesize the available research evidence on innovations that promote IPC in primary care for older rural adults with CD, identify gaps in the literature, and provide recommendations for future research. METHODS: Comprehensive and systematic searches were conducted across four scientific databases for peer-reviewed, original research published in English since 1990, resulting in 9,343 records. Following elimination of duplicates, screening, and evaluation, 38 studies were included for synthesis. All studies were described and illustrated by frequency distribution, and findings were grouped thematically. RESULTS: Most innovations involved case management and focused on diabetes (n = 15), dementia (n = 12), and hypertension (n = 10). Rural challenges were more prevalent than benefits and mainly involved limited services and resources, while strengths were mainly related to close-knit connections and familiarity with one another. Three main themes regarding benefits of the innovations were: 1) enhanced availability/accessibility, 2) earlier detection/management/support, and 3) improved care. Subthemes included: 2a) education/support, 2b) CD or risk factor outcomes, 3a) care continuity, and 3b) care coordination. Five main gaps in the literature included few studies with age-related CDs other than diabetes, dementia, and hypertension; conducted outside of United States and Canada; randomized controlled trial (RCT) and longitudinal studies; that involved virtual or technology-assisted innovations; and that considered sex and gender in the analysis. CONCLUSIONS: Several main areas were highlighted including rural strengths and challenges that impacted the innovations, key innovation benefits, and gaps in the literature. Recommendations for future research were made.
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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 | 0.026 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".