Collaboration among nurse practitioners and registered nurses in outpatient oncology settings in<scp>C</scp>anada
Bibliographic record
Abstract
AIM: This article is a report on a case study that described and analysed the collaborative process among nurse practitioners and registered nurses in oncology outpatient settings to understand and improve collaborative practice among nurses. BACKGROUND: Changes in the health system have created new models of care delivery, such as collaborative nursing teams. This has resulted in the increased opportunity for enhanced collaboration among nurse practitioners and registered nurses. The study was guided by Corser's Model of Collaborative Nurse-Physician Interactions (1998). DESIGN: Embedded single case design with multiple units of analysis. METHODS: Qualitative data were collected in 2010 using direct participant observations and individual and joint (nurse dyads) interviews in four outpatient oncology settings at one hospital in Ontario, Canada. FINDINGS: Thematic analysis revealed four themes: (1) Together Time Fosters Collaboration; (2) Basic Skills: The Brickworks of Collaboration; (3) Road Blocks: Obstacles to Collaboration; and (4) Nurses' Attitudes towards their Collaborative Work. CONCLUSION: Collaboration is a complex process that does not occur spontaneously. Collaboration requires nurses to not only work together but also spend time socially interacting away from the clinical setting. While nurses possess the conceptual knowledge of the meaning of collaboration, findings from this study showed that nurses struggle to understand how to collaborate in the practice setting. Strategies for improving nurse-nurse practitioner collaboration should include: the support and promotion of collaborative practice among nurses by hospital leadership and the development of institutional and organizational education programmes that would focus on creating innovative opportunities for nurses to learn about intraprofessional collaboration in the practice setting.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 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".