Evaluating Tailored Learning Experiences in Emergency Residency Training Through a Comparative Analysis of Mobile-Based Programs Versus Paper- and Web-Based Approaches: Feasibility Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
Background: In the rapidly changing realm of medical education, Competency-Based Medical Education is emerging as a crucial framework to ensure residents acquire essential competencies efficiently. The advent of mobile-based platforms is seen as a pivotal shift from traditional educational methods, offering more dynamic and accessible learning options. This research aims to evaluate the effectiveness of mobile-based apps in emergency residency programs compared with the traditional paper- and web-based formats. Specifically, it focuses on analyzing their roles in facilitating immediate feedback, tracking educational progress, and personalizing the learning journey to meet the unique needs of each resident. Objective: This study aimed to compare mobile-based emergency residency training programs with paper- and web-based (programs regarding competency-based medical education core elements. Methods: A cross-sectional web-based survey (Nov 2022-Jan 2023) across 23 Taiwanese emergency residency sites used stratified random sampling, yielding 74 valid responses (49 educators, 16 residents, and 9 Residency Review Committee hosts). Data were analyzed using Mann-Whitney U test, chi-squared tests, and t tests. Results: MB programs (n=14) had fewer missed assessments (P=.02) and greater ease in identifying performance trends (P<.001) and required clinical scenarios (P<.001) compared with paper- and web-based programs (n=60). In addition, mobile-based programs enabled real-time visualization of performance trends and completion rates, facilitating individualized training (P<.001). Conclusions: In our nationwide pilot study, we observed that the mobile-based interface significantly enhances emergency residency training. It accomplishes this by providing rapid, customized updates, thereby increasing satisfaction and autonomous motivation among participants. This method is markedly different from traditional paper- or web-based approaches, which tend to be slower and less responsive. This difference is particularly evident in settings with limited resources. The mobile-based interface is a crucial tool in modernizing training, as it improves efficiency, boosts engagement, and facilitates collaboration. It plays an essential role in advancing Competency-Based Medical Education, especially concerning tailored learning experiences.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".