Perceptions of the Role of the Registered Nurse in an Urban Interprofessional Academic Family Practice Setting
Bibliographic record
Abstract
Registered nurses (RNs) in Ontario have been asked to work collaboratively with family physicians (FPs) and other healthcare professionals in the family practice setting to improve the efficiency and effectiveness of healthcare delivery (OFPN 2005). Yet, little is known about the optimal utilization of the RN's role in family practice. This study builds on recent conversations regarding utilization of the nursing workforce (Oelke et al. 2008) and the nursing role (White et al. 2008) in the acute care setting by presenting perceptions of the role of the RN in an urban academic family practice setting. Interviews were conducted with 23 healthcare professionals of varying disciplines across three interprofessional academic family practice units in a Canadian city. Interviewees were asked about their perception of the RN's role as it relates to interprofessional collaboration (IPC). Our findings suggest that ambiguity surrounds the RN's role in family practice in general and in IPC in particular. Also, an FP's level of trust in an RN was found to be a central theme and an important variable in determining FP-RN collaboration, with higher levels of RN trustworthiness associated with higher levels of FP-RN collaboration. Optimal utilization of the family practice RN requires leadership in clarifying the RN's role in IPC, and why and how trust among IPC members is cultivated and nurtured.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".