Implementation of competence-based Georgian-Norwegian master program in public health
Bibliographic record
Abstract
Background High quality education is a key prerequisite for the effective public health activities. The importance of competency-based education in public health is widely recognized in past decade, thus core competencies have been developed in Europe, UK, USA, Canada, Australia. In 2016 in Georgia has started project, Georgian-Norwegian Collaborative in Public Health’’ founded by the Norwegian center for international cooperation in education. The goal of the project is to increase public health competence in Georgia by developing a new, high-quality master program in partnership with Arctic University of Norway. Methods The aim of the survey was to explore the attitude of the stakeholders (students, academics, graduates and employers) towards the generic and specific competencies. The survey was created in line with the European Tuning methodology. The question answers ranged from not important to most important on a 4-point Likert scale. Additionally five competencies were selected as most and least important from this scale regardless of which category they represented. Survey was carried out in July-September 2016.In total 240 surveys for the study was analyzed. Results Most and least important five competencies, mean of importance and correlations between the importance's for all groups of respondents were identified. According to the students and academics the most important competence from the generic competencies is, Ability to apply knowledge in practical situations’’, while according to the graduates and employers most important is, Ability for abstract thinking, analysis and synthesis’’. The top specific competencies are different for each group of respondents. The correlation coefficient is highest among employers and graduates (0.958). Conclusions In order to close the gap between university education and competences required in practice new competence-based master program was elaborated at Tbilisi State University based on the survey results. Key messages: This approach can be used in countries in transition for designing the competence-based educational programs in public health. This approach can be used in countries in transition for elaboration reference point in public health 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 | 0.006 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".