Problems and challenges of future medical education: current state and development prospects
Bibliographic record
Abstract
The system of medical education has demonstrated varying degrees of effectiveness around the world. Two approaches are dominant - the traditional approach, which consists of students acquiring knowledge, and the innovative approach, which implies a greater role of practical training. The traditional approach dominates in Asian countries, Russia, and Ukraine. The innovative approach is actively implemented in Western countries (USA, European Union). The study aimed to analyze the level of medical education among the most effective systems. For this purpose, 703 articles were analyzed, 41 of which formed the basis of this study. The most effective systems turned out to be French and Canadian. Within the education of these systems, the duration of education can be reduced to 3 years without losing the quality of education. The features of the innovative system of medical education are the selection of applicants, the duration and structure of the educational program, the methods of teaching medical disciplines. This result is possible due to the application of the small group effect with an emphasis on individual search, interpretation of information, and its application in practice. An important factor is that learning to practice begins in the first year. With the help of the test system, students' knowledge is regularly monitored and the opportunity to work in polyclinics as well as with real patients is provided. Based on the information analyzed, we recommend applying the basics of medical education in Canada to students in Pakistan. Certainly, it will help to achieve practical results in a short period of time.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".