Emigration-related attitudes of the final year medical students in Croatia: a cross-sectional study at the dawn of the EU accession
Bibliographic record
Abstract
AIM: To investigate the emigration-related attitudes of final year medical students in Croatia at the dawn of the EU accession in 2013. METHODS: All final-year medical students at four Croatian medical schools (Zagreb, Rijeka, Split, and Osijek) were invited to participate in a cross-sectional survey on emigration attitudes. RESULTS: Among 260 respondents (response rate 61%), 90 students (35%) reported readiness for permanent emigration, 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 earnings (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 belief that the preferred specialty could be obtained in Croatia 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 propensity 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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".