Guías y videos de simulación clínica para el aprendizaje autónomo de enfermería
Bibliographic record
Abstract
Introduction: Nursing teaching must move from a traditionalist teacher-centered perspective to a student-centered approach. The latter promotes autonomous learning where the teacher acts as a mediator, but not as a source of knowledge.Objective: To identify the need to use clinical simulation guides and videos for the autonomous learning of nursing students at the University of Cundinamarca.Material and Methods: Research under the positivist paradigm, with a descriptive design and a quantitative cross-sectional approach conducted during the first quarter of 2020 on a population of 376 students. An intentional non-probabilistic sampling made up of 95 nursing students was carried out. The data were obtained through a self-administered and anonymous survey. A descriptive evaluation of the data was performed.Results: The results show that 75% of the students demonstrated a high level of need to use guides and videos for their autonomous learning; they considered that the procedures carried out in simulated practices provided greater degree of safety and clarity; in addition, 62% assured that it is a relevant tool for nursing learning.Conclusions: A high need to use clinical simulation guides and videos for autonomous learning in nursing students was demonstrated. These resources promote motivation, safety, and caregiving skills, reducing risks in clinical practices. Regular updating is crucial to ensure their usefulness and innovation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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".