The Effect of a Diabetes Nursing Practitioners in Community Health Settings on Patient Outcomes and Cost Savings: Rapid Review
Bibliographic record
Abstract
Background: Community healthcare settings play a critical role in diabetes management, particularly in regions with high prevalence, such as Israel. While nurse practitioners (NPs) or diabetes nurse specialists have demonstrated cost-effectiveness and positive outcomes in hospitals and primary care, their impact in community settings remains underexplored. This review examines the potential of diabetes-focused NPs/diabetes nurse specialists to improve patient outcomes and reduce costs in community healthcare contexts. Methods: Four sources of information were searched, namely Web of Science, PubMed, ScienceDirect, and Google Scholar. Primary studies conducted in community healthcare settings within the past 10 years (2014-2024) using a sample of diabetes mellitus patients cared by NPs or diabetes nurse specialists met the eligibility criteria. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). Results: Initially, 278 studies were identified, and upon screening them against the eligibility criteria, six of them were found to be eligible for review. All were cohort studies using varied research approaches, predominantly comparative analysis, pre-post designs, and observation. Three of the studies were of high quality, two of moderate quality, and one of low quality. It was found that NPs or diabetes nurse specialists have the potential to improve clinical patient outcomes. They are also likely to save the healthcare system costs because they provide the same quality of care and achieve comparable clinical patient outcomes as physicians and physician assistants yet, in jurisdictions like the United States, their compensation is generally lowered than that of physicians. Conclusion: Policymakers should work on the increased deployment of NPs or diabetes nurse specialists to community healthcare settings after assessing cost-effectiveness in their jurisdictions
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".