Getting to the root of it: How do faculty address professional boundaries, role expansion, and intra-professional collaboration?
Bibliographic record
Abstract
Background: Little research exists to guide nursing faculty to respond to the most recent entry-to-practice education changes and subsequent practice and knowledge expansion for nurses. The purpose of this study was to explore the experiences of faculty as they teach about professional boundaries and role clarity to college and university nursing students and how this teaching is connected to in intra-professional collaboration.Methods: This qualitative research study used a critical feminist sociology to analyze interviews and relevant documents. Twenty-five nursing faculty from an Ontario, Canada school were interviewed.Results: Through our analysis we detected two main findings. The first was the activation of hierarchies positioning the university program with more status and legitimacy than the college program, and how this established power relations and impeded nursing education for role clarity. The second was the struggle to articulate the actual differences between the roles and contributions of the Registered Practical Nurse (RPN) and the Registered Nurse (RN) and how this struggle impeded education for effective collaboration and role clarity.Conclusions: Supporting faculty to recognize the distinct and overlapping contributions of each type of nurse can support educational reform that promotes competencies in collaborative care.
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".