Greek and Latin medical terminology instruction in programmes of general medicine at medical schools in the Czech Republic and abroad.
Bibliographic record
Abstract
TITLE: Greek and Latin medical terminology instruction in programmes of general medicine at medical schools in the Czech Republic and abroad. AUTHOR: Aleš Beran DEPARTMENT: Department of Education SUPERVISOR: doc. PhDr. Miroslava Váňová, CSc. ABSTRACT: The thesis seeks to provide a systematic description of the Greek and Latin medical terminology instruction at Czech and foreign medical schools. Its main objective is to build a platform for implementations of instructional innovations. In the first part of the thesis the medical terminology instruction in the Czech Republic is contextualized by giving a historical overview of development of medical terminology and dealing with origins of its instruction at the Faculty of General Medicine in Prague. The view of a present state of the instruction is completed by a profile of a typical medical student, which is based on the questionnaire. The next part of the thesis consists of detailed content analyses of selected Czech and foreign textbooks and their comparison. Based upon these analyses, an original typology of instructional models is set up which can be considered to be the most important theoretical outcome of the thesis. The typology is subsequently used as a tool for description of teaching medical terminology in Austria, Germany, United States, Canada,...
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".