Examining nursing processes in primary care settings using the Chronic Care Model: an umbrella review
Bibliographic record
Abstract
Abstract Background While there is clear evidence that nurses can play a significant role in responding to the needs of populations with chronic conditions, there is a lack of consistency between and within primary care settings in the implementation of nursing processes for chronic disease management. Previous reviews have focused either on a specific model of care, populations with a single health condition, or a specific type of nurses. Since primary care nurses are involved in a wide range of services, a comprehensive perspective of effective nursing processes across primary care settings and chronic health conditions could allow for a better understanding of how to support them in a broader way across the primary care continuum. This systematic overview aims to provide a picture of the nursing processes and their characteristics in chronic disease management as reported in empirical studies, using the Chronic Care Model (CCM) conceptual approach. Methods We conducted an umbrella review of systematic reviews published between 2005 and 2021 based on the recommendations of the Joanna Briggs Institute. The methodological quality was assessed independently by two reviewers using the AMSTAR 2 tool. Results Twenty-six systematic reviews and meta-analyses were included, covering 394 primary studies. The methodological quality of most reviews was moderate. Self-care support processes show the most consistent positive outcomes across different conditions and primary care settings. Case management and nurse-led care show inconsistent outcomes. Most reviews report on the clinical components of the Chronic Care Model, with little mention of the decision support and clinical information systems components. Conclusions Placing greater emphasis on decision support and clinical information systems could improve the implementation of nursing processes. While the need for an interdisciplinary approach to primary care is widely promoted, it is important that this approach not be viewed solely from a clinical perspective. The organization of care and resources need to be designed to support contributions from all providers to optimize the full range of services available to patients with chronic conditions. PROSPERO registration CRD42021220004.
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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 | 0.047 | 0.144 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.043 | 0.034 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".