Barriers and Facilitators to Evidence-Based Nursing in Colombia: Perspectives of Nurse Educators, Nurse Researchers and Graduate Students
Bibliographic record
Abstract
Barriers and facilitators to Evidence-Based Nursing in Colombia: perspectives of nurse educators, nurse researchers and graduate studentsObjective.To identify and describe the perceptions of nursing researchers, educators, and graduate students regarding the barriers to, and facilitators for, EBN in Medellín, Colombia.Methodology.Using a qualitative descriptive design, in-depth semi-structured interviews were conducted with 12 participants associated with a large university faculty of nursing in Medellín, and one member of the National Association of Nurses.Qualitative content analysis was used to analyze the interview transcripts.Results.Several barriers to EBN were reported, including: lack of recognition of nursing as an autonomous profession; a lack of incentives for nurses to pursue advanced education or engage in research; limited availability and utility of nursing evidence; and a lack of communication between academic and clinical practice environments.Perceived facilitators included an increase in nurses pursuing advanced education opportunities; the current healthcare accreditation process; access to international research and research collaborations; and clinical and research partnerships between universities and clinical institutions.Conclusion.Effective implementation of evidence-based nursing practices is a necessity to translate the vast amount of healthrelated research, knowledge, and experience into positive changes in healthcare quality.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".