Nurse practitioner’s perceptions of the impact of the nurse practitioner-led clinic model on the quality of care of complex patients
Bibliographic record
Abstract
AimTo evaluate the organizational processes that influence the quality of care for patients with multimorbidity at nurse practitioner-led clinics (NPLCs). BACKGROUND: People are living longer, most with one or more chronic diseases (mulitmorbidity) and primary healthcare for these patients has become increasingly complex. One response was the establishment of new models of primary healthcare. NPLCs are an example of a model developed in Ontario, Canada, which feature nurse practitioners as the primary care providers practicing within an interprofessional team. Evaluation of the extent to which the processes within NPLC model addressed the needs of patients with multimorbidity is warranted. METHODS: Eight nurse practitioners were interviewed to determine their perception of the quality of care provided to patients with multimorbidity at NPLCs. Interpretive description guided the analysis and themes were identified.FindingsThree themes arose from the analysis, each of which has an impact on the quality of care. The level of patient vulnerability at the NPLCs was high resulting in the need to address social and financial issues before the care of chronic conditions. Dynamics within the interprofessional team impacted the quality of patient care, including NP recruitment and retention, leaves of absence and turnover in staff at the NPLCs had an effect on interprofessional team functioning and patient care. Finally, coordination of care at the NPLCs, such as length of appointments, determined the extent to which attention was given to individual clinical issues was a factor. Strategies to address social determinants of health and for recruitment and retention of NPs is essential for improved quality of care. Comprehensive orientation to the interprofessional team as well as flexibility in care processes may also have positive effects on the quality of care of patients with complex clinical issues.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".