Developing an educational scheme for undergraduate medical Curriculum: the unit of "infertility" as a sample.
Bibliographic record
Abstract
OBJECTIVES: To present our first experience in scheme development based on CPC philosophy in Iran. HYPOTHESIS: One of the most important reasons of an obvious gap between medical education and professional expectations (outcomes) encountered by recent medical graduates is due to applying conventional curricula, which rely on hypothetical-deductive reasoning model. The University of Calgary has implemented a new curriculum which is organized according to 125 ways in which patients may present to a physician. In this study we will present our first experience in scheme development based on CPC philosophy in Iran. METHODS: In 2007, research and clinical center for infertility (Yazd University of medical sciences, IRAN), began developing a full module for infertility (lesson plan) with fourteen components based on the new curricular philosophy. We recruited a scheme of infertility according to a specific way. RESULTS: Thus, at the first step of the module creation, a scheme was made as the most important mainstay of presentation module, i.e. a structured scheme that includes all causative diseases of infertility. CONCLUSIONS: Any effort in the organization of knowledge around schemes including in the domain of infertility would be valuable to meet some of the standards of WFME. Also, development of modules, by the teams composed of experts and students, can improve the quality of medical education.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".