Medical education for rural areas: opportunities and challenges for information and communications technologies.
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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".