Nursing policy and practice in Mongolia: Issues and the way forward
Bibliographic record
Abstract
Global inequality exists in the availability of a nursing workforce, supported evidentially by the ratio, in low-income countries, of only 9.1 nurses per 10 000 people versus 107.7 nurses per 10 000 people in high-income countries. Mongolia is no exception with 42.14 nurses per 10 000 people and a nursing shortage severe enough to endanger patient safety and well-being. This paper details both a policy analysis and contextually well-designed recommendations to strengthen Mongolia's nursing science and practice systems. Obstacles that significantly affect the successful development of nursing and midwifery professions in Mongolia include (1) a lack of strategic planning and regulation; (2) low status of nurses and midwives; (3) absence of professional representation for nurses and midwives; and (4) a dearth of strategic programs for postgraduate training of nurses and midwives. The suggested recommendations include the appointment of a chief nursing office within the government and a cohort of nurse leaders to work to establish a nursing and midwifery board as an independent, professional regulatory body in Mongolia to develop and implement standards to ensure best practice, higher standards of nurse education, and regulate the profession.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".