Understanding the Healthcare Teams’ Experiences of Working with Nurse Practitioners: A Mixed-Method’s Survey Study
Bibliographic record
Abstract
Aim: This study aimed to explore the experiences and perceptions of healthcare team members working with NPs across multiple practice areas within the Fraser Health Authority, a large health authority in British Columbia, Canada. Background: Nurse Practitioners (NPs) are increasingly integrated into healthcare teams, providing advanced clinical expertise, enhancing patient-centered care, and fostering collaboration. However, little is known about how the integration of NPs impacts the team experience. Methods: A mixed-methods study using an online cross-sectional survey with integration of closed and opened ended survey data was employed, involving primarily quantitative questions with optional open-ended qualitative responses from 115 participants. The quantitative data assessed various attributes of satisfaction across multiple units and clinics, while thematic analysis was used to identify major themes from qualitative data. Findings: High satisfaction was noted in urgent care and surgical units, while lower satisfaction emerged in patient assessment transition to home units where NPs served as MRPs. Three key themes were identified: enhanced patient-centered care, collaborative and integrated team dynamics, and advanced clinical expertise and accessibility. Some participants expressed concerns about delays in patient flow due to thorough NP assessments and diagnostics. Conclusion: NPs significantly contribute to patient-centered care, team collaboration, and overall healthcare quality. However, satisfaction with NP integration varies across settings, particularly in units where NPs assume greater responsibility as MRPs. Tailoring NP roles to fit the unique needs of specific healthcare settings, fostering role clarity, and promoting interprofessional collaboration are key to optimizing NP integration and satisfaction. Future research should explore unit-specific dynamics and strategies for improving NP effectiveness, as well as examining the perception of impact on access and flow related to NP assessments and diagnostics.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".