Registered nurses' perceptions of their roles in medical‐surgical units: A qualitative study
Bibliographic record
Abstract
AIM: The aim of this study was to gain insight into the perception of nurses about their roles in medical-surgical units. BACKGROUND: As a result of ever-changing work environments, medical-surgical nurses find it difficult to know and practice according to the full scope of their roles. DESIGN: A qualitative descriptive study. METHODS: Semi-structured individual interviews were conducted with 21 nurses on three campuses of a large tertiary care hospital located in Quebec, Canada. Thematic analysis was used to construe meaning from the interviews. This research adheres to the Standards for Reporting Qualitative Research guidelines and checklist. RESULTS: The data analysis resulted in three main themes: (i) confusion in nurses' roles and scope of practice; (ii) challenges in the continuity of care and (iii) factors affecting the roles of nurses in medical-surgical units. CONCLUSION: Attention must be paid to the care continuum as it represents a critical element for surgical patients' quality and safety of care. RELEVANCE TO CLINICAL PRACTICE: Medical-surgical nurses should understand their roles and the factors that limit their full scope of practice in order to provide and manage complex care situations. Additionally, an interdisciplinary approach is a strategy that may better respond to patients' clinical needs across the surgical journey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".