Chronic Disease Management in a Nurse Practitioner-Led Clinic: An Interpretive Description Study
Bibliographic record
Abstract
Introduction: Nurse practitioner-led clinics (NPLC) represent a model of care that has the potential to enhance primary healthcare delivery to community-dwelling adults who are living with chronic disease by providing greater access and continuity of care and reducing the burden on acute care settings. However, there is limited understanding of nurse practitioners' experiences and perspectives on supporting adults in chronic disease management within an NPLC model of care. Increased understanding would contribute to our ability to evaluate the effectiveness of the NPLC model of care for chronic disease management. Objective: This study explored current chronic disease management practices implemented by nurse practitioners within NPLC throughout the Province of Ontario. Methods: A qualitative study was implemented using interpretive description. In-depth interviews were conducted between January 2021 and May 2021 with nurse practitioners who were practicing within NPLCs across Ontario. Data was analyzed using thematic analysis. Results: Eleven interviews were completed with nurse practitioners who provided care to community-dwelling adults who were managing chronic disease in a range of geographic settings. Resultant themes included: the nurse practitioner role in bridging access to patients who fall between the cracks, benefits of interprofessional care, meeting a patient where they are at, and addressing healthcare system burden. Conclusion: Results suggest that NPLCs are well-positioned to support community-based patients living with chronic disease through provision of on-site interprofessional care, continuity in service provision and increased access to primary healthcare services. This article provides insights into the nurse practitioner led primary healthcare model and how it can facilitate access to services, foster patient self-management and provide a successful alternative model of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 teacher head, 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".