Effect of an educational intervention on nursing knowledge about enteral nutrition therapy: A quasi-experimental study
Bibliographic record
Abstract
Background and objective: Permanent education is an important strategy to improve nurses' knowledge and professional practice. This study assessed the improvement on the knowledge of nurses after attending an educational intervention on enteral nutritional therapy using clinical simulation.Methods: A quasi-experimental study was conducted with 41 nurses with pre-and post-testing. Two clinical simulation scenarios were applied using the National League for Nursing Jeffries Simulation framework: the first was about the indications for enteral nutritional and insertion of the feeding tube, and the second was about enteral feeding monitoring and control of complications. The intervention was developed according to the guideline for reporting evidence based practice educational interventions and teaching. A validated instrument was used to verify knowledge of enteral nutritional therapy. There was a high level of inter-rater agreement.Results: The analysis of the clinical simulation showed a statistically significant difference between the pre- and post-training scores in all domains of the instrument (p < .05). The effect size was large (Cohen's d = 0.946). The educational intervention with two clinical simulation scenarios significantly improved nurses' knowledge of enteral nutritional therapy.Discussion and conclusions: In general, this research provided nurses with improved knowledge regarding the care of patients using enteral nutrition therapy, contributing to the innovation of care with practices based on scientific evidence. Pre-and post-test analyzes showed that nurses had better knowledge scores on enteral nutritional therapy after the educational intervention using clinical simulation. Implications for clinical nursing practice: Educational interventions based on clinical simulation promote clinical reasoning and decision-making within different levels of nursing praxis.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".