Impact of a “Digital Health” Curriculum on Students’ Perception About Competence and Relevance of Digital Health Topics for Future Professional Challenges: Prospective Pilot Study
Bibliographic record
Abstract
Background: The rapid integration of digital technologies in health care has emphasized the need to ensure that medical students are well-equipped with the knowledge and competencies related to digital health. Objective: This study aimed to evaluate the impact of the "Digital Health" curriculum at our university on the perceptions of medical students regarding the relevance of digital health topics for their future professions and their self-assessed competence in these areas. Methods: The "Digital Health" curriculum was introduced at a German university for 2 consecutive semesters. The perceived relevance of topics for their future careers and their subjective competence were evaluated before and after the curriculum using a Likert scale. Furthermore, the practical gamification-based teaching part of the robotics teaching unit was evaluated. In total, 6 months after completing the last semester, a follow-up analysis was performed with questions on the significance of the completed curriculum for current and future professional challenges regarding digital health and suggestions for improvement for innovative teaching. The study was meticulously planned and supported by an approved ethics vote of the local ethics committee to ensure that all ethical guidelines were adhered to (A 2022-0137). Results: A total of 20 students participated, with 13 (65%) being women. In particular, data protection and information security were considered the most relevant topics both before and after the curriculum. Significant increases in perceived importance were observed for messenger apps (mean increase of 0.8 [SD 1.2]; P<.01). Regarding self-assessed competence, significant development was observed on almost all topics. The greatest development was observed in robotics (mean increase of 1.8 [SD 1.2]; P<.001), open educational resources (mean increase of 1.7 [SD 1.5]; P<.001), and simulation-training (mean increase of 1.6 [SD 1.3]; P<.001). The gamification-based, robot-related teaching was predominantly rated suitable and very enjoyable for the students. Conclusions: The results highlight the potential to integrate more innovative teaching techniques, such as gamification, augmented reality, virtual reality, and simulation training, into a technologically advanced health care environment. Finally, the overarching importance of artificial intelligence and digital health applications signals the need to further integrate them, given their potential in remote and personalized medicine.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".