Examining the impact of nurse practitioner-led group medical visits for patients with chronic conditions in primary care
Bibliographic record
Abstract
The aging Canadian population, increasing incidence of chronic conditions, and rising healthcare costs have contributed to concerns that the current healthcare system may not meet healthcare needs. Canada has sought innovative ways to meet patients’ healthcare needs through reforms such as group medical visits (GMVs) and care by nurse practitioners (NPs). While studies have shown that care with NPs and GMVs is effective, there is limited evidence examining how NPs engage in innovative care delivery. The purpose of this study was to examine the impact of NP-led GMVs for patients with chronic conditions in primary care. This study used multiple methods, including a systematic review and meta- analysis and a multisite case study (N=3). The systematic review and meta-analysis included studies published between 1947 and 2012 for patients with type 1 or 2 diabetes who attended GMVs. Of the 94 studies identified, 13 met final inclusion criteria. Group medical visits had a positive effect on clinical and patient-reported outcomes, with significant reductions in glycated hemoglobin (HbA1c reduction −0.46%, 95% confidence interval −0.80% to −0.31%). The case study consisted of two cases where NPs were using GMVs and one where NPs were not using GMVs. Open-ended interviews with patients (N=12), providers (N=14) and 10 hours of direct observation were completed. Analysis of the data suggests that GMVs facilitated an environment that was patient centered, interprofessional and increased patients’ confidence managing chronic conditions. Furthermore, the processes of care within the GMVs disrupted power differentials in primary care, between patients and providers and amongst healthcare providers. Yet, these same power differentials constrained NPs’ ability to adopt GMVs, with NPs indicating that they had limited agency to diffuse healthcare innovations. Unique contributions of this study were a systematic review and meta-analysis of GMVs among those with diabetes and new knowledge on how power differentials influence the diffusion of innovations in primary care. These findings demonstrate that GMVs provide opportunities to meet clinical, team-based, and patient-centered healthcare objectives. Ongoing research that considers the context of practice environments, power differentials, and conditions that limit NPs ability to diffuse healthcare innovative is needed.
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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 | 0.021 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".