Bibliographic record
Abstract
Effective relationship between education and practice is of great importance for medical graduate students.Obviously, graduates of medical sciences should be familiar with the practical application of theoretical knowledge, clinical actions and encounter with patients.Along this, continuing medical education (CME) programs are introduced and implemented in the field of medical education throughout the world.Continuing medical education includes activities aimed at preserving and developing the knowledge, skills and professional performance of medical team members being in line with providing better services to patients, community or profession.The ultimate goal of continuing medical education programs is to improve the quality of patient care through professional training.However, improving the quality of learner's education is not possible without the transformation of methods and teaching techniques.The use of innovative educational methods in the process of continuing education in different countries leads to favorable outcomes which can be very effective for other countries.In this research, the common and effective methods of medical education are introduced by reviewing the contexts of medical sciences.These methods include lectures, collaborative patterns, group discussions, problem solving, e-learning, clinical education, evidence-based medicine, and medical-based simulations.According to the aforementioned methods, three methods of clinical education, evidence-based medicine and medical-based simulations are specific to medical science, and the rest of the methods are common with other disciplines.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.971 | 0.975 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".