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Record W2768464360

FOREIGN DOCTORS AND FOREIGN PATIENTS. LEARN AND TREAT YOURSELF IN ROMANIA

2014· article· en· W2768464360 on OpenAlexaboutno aff
Ruxandra Dinulescu, Laura Violeta Voicu

Bibliographic record

VenueRePEc: Research Papers in Economics · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicEducation, Management, Technology, Human Resources
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology
DOInot available

Abstract

fetched live from OpenAlex

If the medical tourism is issued mainly from an industrialized area with a significant purchasing power, like USA, Canada, Great Britain or West Europe, the students who come in Romania to study medicine, arrive from countries like Tunisia, Iran, Palestine, Morocco, etc. No matter what countries provide, on one hand, medical tourism and on the other hand, foreign students, what is for sure is the fact that Romania has a big advantage, mainly because these human resources represent a capital and an investment for our country.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0310.007

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 head0.062
GPT teacher head0.363
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2014
Admission routes1
Has abstractyes

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