Clinical nursing and midwifery research in Latin American and Caribbean countries: A scoping review
Bibliographic record
Abstract
AIM: To identify and describe published, nursing-led and midwifery-led, clinical research that has been conducted in Latin America and the Caribbean. BACKGROUND: Peer-reviewed published research may correspond to and elucidate country's realities, priorities, and needs. DESIGN: A 6-stage scoping review methodology was used to search scientific databases using an applied search strategy. DATA SOURCES: Five databases were searched for articles published in English, Spanish, or Portuguese conducted in a Latin American or Caribbean country between January 1, 2006 and June 14, 2016. REVIEW METHODS: Articles were independently considered for inclusion by 2 researchers, data extracted, and study characteristics described. RESULTS: Of 6922 articles identified, 404 were included. The majority were conducted in Brazil (90.6%) followed by Chile (2.5%). Most were nurse-led (95.8%) and were implemented in hospitals (48.6%). Studies frequently explored patient knowledge or characterized patient populations (61.3%) and commonly assessed chronic disease (19.3%) or maternity/child health outcomes (15.9%). CONCLUSION: Findings revealed a large number of publications but an uneven geographical distribution of nurse-led clinical research and an evident gap of midwifery-related research in Latin America and the Caribbean. Results may be used to build research agendas to promote nursing and midwifery research capacity and further establish evidence-based practice.
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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.027 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| 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".