Nursing research in Latin America: priorities and possible solutions to move it forward
Bibliographic record
Abstract
Nursing research has allowed for the evolution of the discipline in areas such as the study of the ontology of care, the organization of health services, the organization of schools and faculties, nursing education, and nursing interventions and practices. Although nursing research has been growing in Latin America, it does not compare with the research being conducted in other health disciplines, and the need for nursing research in the region is still great. This article is the result of a reflection on nursing research in Latin America and aims to identify some of the research priorities in this region and possible solutions. The main priorities for nursing research are related to nursing interventions and innovations. There is also a need for more research in partnership with vulnerable groups and further research on public policy needs, interprofessional collaboration and practice, and nursing human resource planning. To address these priorities, it will be necessary to facilitate the involvement of community stakeholders and clinical practice nurses as well as the development of collaborations between researchers from different Latin American countries. Considering the complexity and diversity of the contexts in which nurses in Latin America work, it will also be necessary to develop nursing theories specific to regional contexts. By implementing some of the solutions proposed in this article, it may be possible for nursing research to further develop its potential to address many health challenges in Latin America.
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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 | 0.107 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".