The Registered Nurse Role in Primary Care: A Focused Ethnography
Bibliographic record
Abstract
Background: The World Health Organization’s 2020 report on the state of the world’s nursing stated that efforts should be made to optimize the role of the registered nurse (RN) in primary care. Research evidence supports that RNs in primary care are underutilized and have unclear role descriptions (Lukewich et al., 2014). Aim: The purpose of this study was to further explore the RN role in community-based multidisciplinary primary care settings in Ontario. The research questions were: (1) What are the shared beliefs, values, and practices of RNs in primary care settings? and (2) What facilitates, constrains and/or sustains the RN role in primary care settings? Methodology: This focused ethnographic study was guided by critical realism. Data Collection: Data was collected through a socio- demographic questionnaire, two individual virtual interviews conducted with each participant, and analysis of publicly posted job advertisements for primary care RNs. Results: Through Roper and Shapira’s (2000) method of data analysis participants shared beliefs, values, and practices as primary care RNs were identified as: holistic patient-centered care, seeing patients make positive changes, practicing autonomously, bridging gaps, and collaboration. Factors which constrain the RNs ability to practice within their values and beliefs included workload and role obscurity while collaboration and the nurse-client relationship facilitated their role. The findings highlight the importance of defining the primary care RN role according to their distinct values, beliefs, and scope of practice and how without this the role may be subject to work intensification and deprofessionalization.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".