Development of Students-Foreigners’ Communicative Competence by Means of Information Technologies under the Conditions of the Medical University
Bibliographic record
Abstract
The article deals with an urgent issue of a systematic research of foreign students studying in Ukraine. Successful learning and social adaptation of young people greatly depend on learning the Ukrainian language. In addition to the obvious advantages, it is advisable to pay particular attention to the system of theoretical and methodological support of the educational process, starting from the author’s textbooks and finishing with modern information technologies. Forming the communicative competence of a medical professional is impossible without taking into account the value-motivational side of this process. In this regard, the communicative training of the healthcare worker is viewed within an axiological approach. As a consequence, professional values are the central category. Peculiarities of future doctors’ professional values formation as a component of communicative competence are considered. The study, aimed at analysing the development of professional qualities of foreign medical students, was conducted at I. Horbachevsky Ternopil National Medical University in the 2018-2019 academic year.The study was carried out under the guidance of professors A.Vykhrushch and N. Fedchyshyn by lecturers of I Horbachevsky Ternopil National Medical University T. Khvalyboha (EdD), I. Drach (PhD), M. Rudenko (PhD) that teach students-foreigners at the Department of the Ukrainian Language and Department of Foreign Languages.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".