Medical education for rural areas: opportunities and challenges for information and communications technologies.
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
Resources in medical education are not evenly distributed and access to education can be more problematic in rural areas. Similar to telemedicine's positive influence on health care access, advances in information and communications technologies (ICTs) increase opportunities for medical education. This paper provides a descriptive overview of the use of ICTs in medical education and suggests a conceptual model for reviewing ICT use in medical education, describes specific ICTs and educational interventions, and discusses opportunities and challenges of ICT use, especially in rural areas. The literature review included technology and medical education, 1996-2005. Using an educational model as a framework, the uses of ICTs in medical education are, very generally, to link learners, instructors, specific course materials and/or information resources in various ways. ICTs range from the simple (telephone, audio-conferencing) to the sophisticated (virtual environments, learning repositories) and can increase access to medical education and enhance learning and collaboration for learners at all levels and for institutions. While ICTs are being used and offer further potential for medical education enhancement, challenges exist, especially for rural areas. These are technological (e.g., overcoming barriers like cost, maintenance, access to telecommunications infrastructure), educational (using ICTs to best meet learners' educational priorities, integrating ICTs into educational programs) and social (sensitivity to remote needs, resources, cultures). Finally, there is need for more rigorous research to more clearly identify advantages and disadvantages of specific uses of ICTs 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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