Evaluating the Use of a Note-Taking App by Japanese Resident Physicians: Nationwide Cross-Sectional Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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