E-LEARNING IN PRE-GRADUATE HEALTHCARE TRAINING – GOOD PRACTICE ACROSS THE WORLD
Bibliographic record
Abstract
E-learning is a modern technological approach to creating active and constructive learning with a leading role of the student, 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 and Australia, 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 blended 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.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".