The role of NPs in preventing readmissions and emergency department visits among individuals with complex needs
Bibliographic record
Abstract
Context: As nurse practitioners (NPs) are increasingly integrated into healthcare systems globally, understanding their role in combating prominent healthcare challenges of readmissions and emergency department visits is integral to finding innovative solutions. There is evidence to suggest that access to high-quality continuous care is essential in the prevention of readmissions and emergency department visits for patients with complex care needs. Primary care becomes the ideal intervention point, and the prevention of readmissions and emergency department visits are targeted parameters to measure success. Objective: To examine the role of NPs in preventing hospital readmissions and emergency department visits among patients with complex care needs, from the perspective of patients, NPs, and physicians. Study Design: Qualitative description. Setting: Semi-rural Canadian province, located on the east coast. Within this region, NPs practice as Primary Health Care NPs, with no specializations currently recognized. Population studied: Participants included patients with complex care needs, NPs, and physicians. Results: Three overarching themes and several subthemes emerged, these included: Multifaceted Role (Educator, Gatekeeper, Navigator, and Collaborator), Approach to Care (Anticipatory Care, Flexible and Adaptive, Therapeutic Relationship, Patient-Centred Care, and Holistic Care), and Time as an Asset. Encompassing a variety of roles was found to be critical to preventing hospital readmissions and emergency department visits among patients with complex care needs, however, this alone was not enough. Foundational to combating this issue is an approach to care that that is not only anticipatory and improves accessibility, but also fosters patient engagement and self-management. This approach, combined with the ability to devote time to each patient interaction, allow nurse practitioners to peel back the layers of complexity and explore the “bigger picture”. No one size fits all solution exists to solving the challenges of hospital readmissions and emergency department visits. Conclusion: This study has the potential to inform future practice and policy, as NPs become increasingly integrated into healthcare systems. Examining NPs’ unique contributions to healthcare challenges can potentially inform future deployment of advanced practice nursing roles.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".