Bibliographic record
Abstract
www.cmj.hr Aim To investigate the emigration-related attitudes of final year medical students in Croatia at the dawn of the EU ac-cession in 2013. Methods All final-year medical students at four Croatian medical schools (Zagreb, Rijeka, Split, and Osijek) were in-vited to participate in a cross-sectional survey on emigra-tion attitudes. Results Among 260 respondents (response rate 61%), 90 students (35%) reported readiness for permanent emigra-tion, expecting better quality of life (N = 22, 31%), better health care organization (N = 17, 24%), more professional challenges (N = 10, 14%), or simply to get a job (N = 8, 11%), while the least common expectation were greater earn-ings (N = 7, 10%). The most common target countries were Germany (N = 36, 40%), USA and Canada (N = 15, 17%), and UK (N = 10, 11%). In a multivariate analysis, readiness for permanent emigration was associated with an interest in undertaking a temporary training abroad (odds ratio [OR] 6.87; 95 % confidence interval [CI] 2.83-16.72), while the be-lief that the preferred specialty could be obtained in Croa-tia appeared protective against emigration (OR 0.26; 95% CI 0.12-0.59). Conclusion Despite shortages of health care workers in Croatia, the percentage of students with emigration pro-pensity was rather high. Prevalent negative perception of the Croatian health care and recent Croatian accession to the EU pose a threat of losing newly graduated physicians to EU countries.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.926 | 0.816 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".