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
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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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".