Evaluating the Use of a Note-Taking App by Japanese Resident Physicians: Nationwide Cross-Sectional Study
Bibliographic record
Abstract
Background: Note-taking is a method that has long been used to optimize studying. Recent innovations have seen the introduction of digital note-taking using software apps. Although the current state of digital note-taking has been verified mainly among students, the use and efficacy of digital note-taking by physicians in actual clinical practice remain unknown. Therefore, we sought to understand the characteristics of note-taking residents using a note-taking app and determine whether there is a difference in basic medical knowledge compared to that of nondigital note-taking residents. Objective: This study investigated the use of a digital note-taking app by Japanese resident physicians. Methods: This analytical cross-sectional study was conducted in resident physicians during the General Medicine In-Training Examination (GM-ITE), a clinical competency examination for resident physicians. The GM-ITE is a multiple-choice test with a maximum score of 80 points. Using a structured questionnaire, we collected data on the sociodemographic characteristics (sex, age, postgraduate year [PGY], or others), clinical training, GM-ITE scores, and the use of an app for note-taking to record case experience. The GM-ITE evaluated the scores by dividing them into 4 groups (groups 1-4), in order from the lowest to the highest. We conducted a multivariate analysis of sociodemographic, clinical training, and GM-ITE score variables to determine the independent predictors of the use of a digital note-taking app. Results: This study included 3833 participants; 1242 (32.4%) were female, 1988 (51.8%) were PGY 1 residents, 2628 (68.6%) were training in a rural area, 3236 (84.4%) were in community-based hospitals, and 1750 (45.3%) were app users. The app users were more likely to be in their PGY 2, to work in a community-based hospital, to have general internal medicine rotation experience, to use online medical resources more frequently, and to have more time for self-study. The results showed that the app users group had a higher GM-ITE score than the nonapp users group (adjusted odds ratio 0.74, 95% CI 0.25 to 1.22; P=.003). Conclusions: To the best of our knowledge, this is the first study to investigate note-taking by physicians in Japan using apps. The app users group had a higher GM-ITE score than the nonapp users, suggesting that they may have higher clinical skills. In the future, we would like to conduct more in-depth research on the facts of note-taking using apps, based on our results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".