E-LEARNING IN HEALTHCARE EDUCATION - EXPERIENCE OF THE DEVELOPED COUNTRIES
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
E- learning is a modern technological approach to creating active and constructive learning with a leading role of the student. It has advantages that traditional education does not offer and is integrated in modern university education. It is developing extremely dynamically in health care because of the benefits it offers. A review has been done of educational initiatives, connected with pre-graduate training of healthcare specialists in developed countries - the United States, Canada, Australia and Great Britan, where it has been offered since the beginning of the century and there are established traditions. The aim is to study good practices and highlight problems in the design of electronic forms. Publications in English from referred sources are investigated. The following key issues are outlined: healthcare education has specific features that affect the use of electronic forms; the combined option is the most appropriate - face-to- face and e-learning; effectiveness is directly dependent on the quality of resources; it can be applied at each stage of the training - from the theory to the patient's bed; students have positive attitudes; teachers take on new roles and responsibilities. E-learning is an expensive and labor-intensive initiative and is created by multi-professional teams after analyzing the students` pedagogical characteristics. Virtual training must be in line with the development strategy of the university and requires understanding and engagement of policy makers.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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