Bibliographic record
Abstract
The underrepresentation of racial and ethnic minority nurses and other health professions has been linked to the continued disparities in health outcomes for these populations (Institute of Medicine. Unequal Treatment. Confronting Racial and Ethnic Disparities in Health Care. Washington, DC: The National Academies Press; 2002). Strategies to reduce health disparities, such as increasing the cultural competence of majority nurses, often depend on leaders of the same racial and ethnic groups to provide leadership in research, education, practice, and communities. However, little is known about the leadership pathways of racial and ethnic minority nurses. In this descriptive study, 22 Hispanic nurse leaders completed an open-ended survey related to their definitions of leadership, and challenges and opportunities in their leadership development. Although there are similarities in many aspects of leadership development, the lack of Hispanics in the workplace, the ability and responsibilities related to being bilingual and bicultural, and perceived discrimination in the work setting are additional challenges identified by Hispanic nurses. The importance of role models and mentors in facilitating leadership development is a dominant theme. Results of this study provide direction not only for the development of leadership for Hispanic nurses but also for other racial and ethnic minority nurses.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".