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Brain drain: final year medical students’ intentions of training abroad

2015· article· en· W2330313054 on OpenAlexaboutno aff
Ana Bojanić, Katarina Bojanić, Robert Likić

Bibliographic record

VenuePostgraduate Medical Journal · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBrain drainTraining (meteorology)Medical education

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.177
GPT teacher head0.516
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2015
Admission routes1
Has abstractyes

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