Debating the Role of Smartphones and Mobile Applications in Medical Education
Bibliographic record
Abstract
Mobile devices have become pervasive within our society.From telecommunications to social media to professional networking platforms, mobile devices are considered a necessity by their users.With the rapid pace of technology innovation and the general evolution of medicine, it would be expected that digital learning platforms, including mobile devices, have also entered the field of medical education.The literature supports the use of mobile devices and medical mobile applications, as a supplement to traditional educational modalities, facilitating access to online medical textbooks, webcasts/podcasts, and online asynchronous classroom.These technologies have the potential of enabling learner-centered and situational learning.However, despite reported benefits there are still concerns that mobile applications focus on lower levels of learning, such as knowledge attainment, with little benefit towards higher levels of Bloom's taxonomy, such as critical thinking.Additionally, only a small percentage of the mobile applications are regulated or accredited by governmental organizations or medical associations, which underscores the concerns regarding content quality and acceptance of its use in medical education.To address these concerns, the following paper will review and highlight the benefits and risks of mobile devices and medical applications as educational tools in medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".