Bibliographic record
Abstract
Nursing is a respected profession that aims to blend scientific knowledge and specialized skills to deliver high-quality healthcare services to patients and other individuals. In the Sultanate of Oman, a country situated on the southeastern coast of the Arabian Peninsula in Western Asia, nurses have the responsibility of taking care of patients and maintaining the quality of nursing care. The Oman government considers nursing to be a vitally significant profession, to be developed continuously as part of a vibrant healthcare topography of continuous improvement. This ethos underpins work on Oman’s standards of higher education and international standards of nursing education (Al Maqbali et al., 2019) Nurses in Oman make up the majority of the healthcare force; and approximately 60% of all healthcare professionals ( Ministry of Health, 2022). An educated and competent nursing workforce is vital for meeting national demand and maintaining quality in healthcare. Omani colleges and institutes with nursing faculties recruit doctoral nursing faculty members from different countries, and the MoH has collaborated with the Ministry of Higher Education (MHERI) to sponsor many Omani nursing professionals to obtain their PhD degrees from respected global universities, including in Australia, Canada, Ireland, the UK, and the USA. Despite the development of nursing and nursing education in Oman, like other counties, it faces a global shortage of nursing practitioners and academics. Currently, there are three universities in Oman implementing undergraduate nursing education under the umbrella of higher education and awarding bachelor’s degrees. An MSc in nursing is now offered at the national flagship higher educational institution, Sultan Qaboos University. In addition, The MoH has a College of Health Sciences based in Muscat, with eight branches in each region of Oman, offering bachelor’s degrees in nursing. In addition, there is a Higher Institute of Health Specialties (HIHS) in Muscat offering specialty postgraduate diplomas in different specialties for graduate registered nurses. It has nine nursing programs: Adult Critical Care Nursing (ACCN), Emergency Nursing (EN), Critical Care Nursing in Pediatric and Neonatology (CCNPN), Nephrology Nursing (NN), Midwifery Nursing (MN), Mental Health Nursing (MHN), Community Health Nursing Practice (CHNP), Infection Prevention and Control (IPC), and Health Services Management (HSM). From the above list, the ACCNP was particularly designed to improve the knowledge, skills, and attitudes of nurses working in critical care areas, such as the intensive care unit coronary care unit and post-cardiac surgery units, and to maximize the quality of care. ACCNP was initiated in the academic year 2004-2005, in partnership with faculty members from the University of Central Lancashire in the UK for three years (2004 to 2007), after which it was administered fully by the ACCNP faculty members at HIHS. The curriculum of ACCNP covers different competencies required for graduates, aligned with the main graduate attributes of HIHS. The impact of the training is measured by HIHS for each cohort, to maintain quality standards and improve the learning and teaching process. References Al Maqbali MR, Al Omari O, Slimane SBA, Balushi NA. The nursing profession in Oman: An overview. Nursing Science Quarterly. 2019;32(4):322–325. https://doi.org/10.1177/0894318419864346 Ministry of Health. Annual health report, 2022. https://www.moh.gov.om/documents/274609/7264771/Annual+Health+Report+2022/47623227-57f9-d9b7-372b-f16d8af6d91f
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".