Validity and Reliability of the Japanese Version of the ACE Tool for Assessing Evidence-based Medicine Competencies in Medical Practitioners and Students: An Evaluation in an Online Setting
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Bibliographic record
Abstract
Objective Evidence-based medicine (EBM) competency is crucial for healthcare professionals; however, validated tools to assess EBM skills in Japanese are scarce. This study aimed to develop and validate a Japanese version of the Assessing Competency in EBM (ACE) tool. Methods We translated the ACE tool into Japanese, following international standards, and distributed it online to 99 healthcare professionals and students. The participants completed demographic questions and the Japanese version of the ACE tool. A subset also completed the retest and Fresno test. Internal consistency was assessed using Cronbach's alpha, test-retest reliability using the intraclass correlation coefficient (ICC), and construct validity using a confirmatory factor analysis and correlation with the Fresno test. Results The Japanese version of the ACE tool showed a low internal consistency (Cronbach's alpha =0.31, 95% CI: 0.09-0.49), but an acceptable test-retest reliability (ICC =0.64, 95% CI: 0.40-0.81). A confirmatory factor analysis provided moderate support for the structure of the tool (SRMR =0.092, RMSEA =0.048, CFI =0.852). The tool demonstrated a moderate correlation with the Fresno test (r =0.35). The median completion time was 847 s (IQR, 577-1,249 s). Conclusion Although the Japanese version of the ACE tool showed some promising aspects, including a quick administration and partial validity, its low internal consistency suggests that refinement is needed before it can be confidently used in Japanese medical education settings. Future studies should focus on improving the tool's reliability, potentially through in-person administration, to develop a robust EBM assessment tool in the Japanese healthcare context.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it