Nurse-Led Models of Care for Patients with Complex Chronic Conditions: A Scoping Review
Bibliographic record
Abstract
The burden of complex, chronic conditions in Canadian healthcare is growing, requiring more human and financial resources than ever before. It has become increasingly paramount to look for new ways to more effectively manage complex care to meet the needs of patients and providers. Nurse-led models, including advanced practice models, are uniquely positioned to pioneer innovative care delivery methods for patients with complex chronic needs in Canada. A scoping review was undertaken to determine what is known about nurse-led models of care for patients with complex chronic conditions. Nurse-led models of care include not only nurses independently managing complex care but also nurse practitioners, clinical nurse specialists and other specialist nurses. Using the Arksey and O'Malley framework for scoping reviews, 35 publications were identified in the search. Although the academic literature was surprisingly limited, our results suggest that nurse-led models are feasible opportunities to better coordinate care of patients with complex chronic conditions. Specific aims of nurse-led models of care focused on patients with more than one condition were identified in the review. These findings highlight the need to continue to explore nurse-led models of care as a strategy to facilitate a more coordinated and systematic approach to chronic care delivery.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".