Bibliographic record
Abstract
Nurses are the largest workforce that impacts the health of the people. As the life expectancy and the number of people with chronic diseases increases in Asia, it is important for Asian nursing professionals to play a pivotal role in improving health status and quality of life by demonstrating their leadership. This article focuses on nursing leadership in Korea. Leadership exemplars were described in detail to show how successful nursing leaders set goals and challenged to meet these goals, despite numerous obstacles that exist in male-oriented society of Korea. And three essential elements that future nurse leaders should consider were suggested. First, awareness was emphasized as the first step to becoming a leader. Clear awareness about women and nursing would help nursing leaders reduce or eliminate gender biases that can undermine nurses' achievements and limit their advancement. Second, challenge was stressed as nursing leaders must challenge and expand the horizon of nursing by facing their situations and problems with courage. Lastly, transformation was highlighted, as the ultimate role of a leader is to make the world better. Four leadership frames-structural frame, human resource frame, political frame, and symbolic frame-were introduced to help guide their leadership journey to change the world. The results of this study would help nurses innovate with excellent leadership by efficiently managing nursing resources and by helping nursing organizations to adjust to changing demands in health care.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".