Use of simulation-based learning in Japanese undergraduate nursing education: National survey results
Bibliographic record
Abstract
Introduction: Simulation-based learning (SBL) is a practical and efficient learning method that involves the replacement of a portion of clinical education with quality simulation experiences. It has been utilised in various countries, such as the United States, Canada, and South Korea. However, based on current regulations in Japan, clinical education cannot be replaced with simulation experience. For future curriculum integration, it is necessary to clarify the current use of SBL and tackle systematic educational strategies of SBL. Therefore, this national survey aimed to clarify the prevalence and practices of SBL in undergraduate nursing education programs in Japan. Methods: This article presents the results of our national survey in Japan. It presents the questionnaire based on the International Nursing Association for Clinical Simulation and Learning Standards of Best Practice and demonstrates the use of simulation-based learning in Japanese undergraduate nursing programs. Results: Overall, the schools using simulation-based education (SBE) comprised 346 schools (82.4%) of the sample. Those equipped with high-fidelity simulators were 146 schools (27.6%); the rest owned medium-fidelity simulators. Almost all undergraduate nursing education systems were equipped with simulators, however, the frequency of use was low. SBL was incorporated into the curriculum at many undergraduate nursing education institutions, and awareness of the INACSL Standard of Best Practice: SimulationSM was extremely low. Conclusion: This study shows that SBL is not properly utilised in undergraduate nursing programs, even though many schools are equipped with simulators. Thus, further study on barriers to simulator use is needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".