Bibliographic record
Abstract
Medical education is a leading step in improving the quality of health services all over the world; in recent years such an important issue has changed tremendously. New concepts and theories were introduced particularly in undergraduate medical education, these may include beyond curriculum education and the concept of problem based learning (PBL). The latter was introduced by Barrows at McMaster University, Canada over three decades ago and will gain the main emphasis in this article. PBL has shown to be valuable and reflects major improvements in undergraduate medical education. Incorporation of such changes will rarely bear priority in developing countries such as Libya, where the debate about the challenges of undergraduate medical education and the importance of the problem-based learning has just started. However, every one who is involved in medical education has his own views about the process of teaching and that tends to reflect on the way he teaches.Here are my personal views about undergraduate medical education which will be discussed by answering three main questions: 1) How did I develop my views? 2) What are my views? 3) How these views affect the way I teach?
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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".