The Effect of Competency-Based Education on Medical and Nursing Students' Academic Performance, Technical Skill Development, and Overall Satisfaction and Preparedness for Future Practice: An Integrative Literature Review
Bibliographic record
Abstract
Purpose: This article provides an integrative review of competency-based education (CBE) in medical and nursing programs and examines the effect of CBE on students’ academic performance, technical skill development, and overall satisfaction and preparedness for future practice. Background: In recent decades, CBE has increasingly been discussed in medical and nursing education programs. The impact of the CBE curriculum on learning outcomes including academic performance, technical skill development, overall satisfaction, and preparedness for future practice has not been fully elucidated. Method: A review of the literature was conducted, and multiple databases were searched for studies that analyzed the impact of CBE on learning outcomes in medical and nursing program learners. Results: The overall trends in feedback showed that CBE was well-received by students, with high satisfaction scores reported. CBE was also shown to be equally or more effective than the traditional didactic model in developing students’ competencies and improving academic and clinical performance. Conclusion: Our comprehensive review of the literature suggests that competency-based education can be an effective framework that potentially outperforms traditional educational approaches on outcome measures related to clinical knowledge, technical skill, and/or clinical judgement.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".