Brain drain: final year medical students’ intentions of training abroad
Bibliographic record
Abstract
BACKGROUND: In Croatia, a new European Union (EU) member state since July 2013, there is already a shortage of around 3280 doctors to reach the European average. OBJECTIVES: To investigate the emigration intentions of the current cohort of final year medical students at Zabreb School of Medicine. METHODS: An electronic questionnaire was used in June 2013 to assess the attitudes of 232 final year medical students towards working conditions abroad and expectations for career opportunities in Croatia following accession to the EU. RESULTS: With an overall response rate of 87%, more than half of the surveyed students (106/202, 53%) intended to travel abroad, either for specialty (52/202, 26%) or subspecialty (54/202, 27%) training. More female students (58/135, 43%) than male students (17/62, 27%) indicated they would not emigrate. Most attractive emigration destinations were: Germany (34/121, 28%), USA (19/121, 16%), the UK (19/121, 16%), Switzerland (16/121, 13%) and Canada (11/121, 9%). The most important goals that respondents aimed to achieve through training abroad were to excel professionally (45/120, 38%), to prosper financially (20/120, 17%) and to acquire new experiences and international exposure (31/120, 26%). CONCLUSIONS: Students' motivating factors, goals for and positive beliefs about training abroad, as well as negative expectations regarding career opportunities in Croatia, may point towards actions that could be taken to help make Croatia a country that facilitates medical education and professional career development of young doctors.
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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 | 0.007 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".