Role development and utilization of master’s-prepared omani nurses working in clinical settings: A multiple case study
Bibliographic record
Abstract
Background and aim: The roles of master’s-prepared nurses in clinical settings differ by country. Examples include nurse practitioners, clinical nurse specialists, nurse managers, educators, and researchers. Little is known regarding the role development and utilization of master’s-prepared nurses in Oman. The aim is to explore the role development and utilization of master’s-prepared Omani nurses working in clinical settings in Oman’s governmental healthcare system.Methods: Multiple case study methodology involving two governmental acute care hospitals in Oman. Individual semi-structured interviews were conducted with master's-prepared Omani nurses (n = 19), policymakers (n = 8), and co-workers (n = 8). Relevant documents, including job descriptions and nursing career pathway were reviewed. Interviews and documents were analyzed using thematic analysis.Results: Master's-prepared Omani nurses were mainly utilized in management, education, and nurse specialist roles. Four overarching themes were identified: 1) Drivers for master's-preparation in clinical settings, 2) The journey after pursuing a master's education, 3) Master’s- prepared nurses' roles, their development, and their scope of practice, and 4) Perspectives about the current utilization of master's-prepared Omani nurses. Participants indicated utilization of master’s-prepared Omani nurses in clinical settings could be enhanced.Conclusions: Master’s-prepared nurses could play a vital role in supporting the needs of patients and addressing gaps in clinical settings if their advanced knowledge and competencies were fully utilized. Linking master’s-prepared nurses’ roles to patient, organization, and system needs and engaging stakeholders in developing their roles will enable optimum utilization of this valuable human resource.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".