Mobile technologies in medical education: AMEE Guide No. 105
Bibliographic record
Abstract
Mobile technologies (including handheld and wearable devices) have the potential to enhance learning activities from basic medical undergraduate education through residency and beyond. In order to use these technologies successfully, medical educators need to be aware of the underpinning socio-theoretical concepts that influence their usage, the pre-clinical and clinical educational environment in which the educational activities occur, and the practical possibilities and limitations of their usage. This Guide builds upon the previous AMEE Guide to e-Learning in medical education by providing medical teachers with conceptual frameworks and practical examples of using mobile technologies in medical education. The goal is to help medical teachers to use these concepts and technologies at all levels of medical education to improve the education of medical and healthcare personnel, and ultimately contribute to improved patient healthcare. This Guide begins by reviewing some of the technological changes that have occurred in recent years, and then examines the theoretical basis (both social and educational) for understanding mobile technology usage. From there, the Guide progresses through a hierarchy of institutional, teacher and learner needs, identifying issues, problems and solutions for the effective use of mobile technology in medical education. This Guide ends with a brief look to the future.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.041 |
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".