Bibliographic record
Abstract
Abstract The world is witnessing mobility of human resources facilitated by globalization and by international agreements on trade in services GATS. The advanced demographic transition in developed economies and increase in aging population have put pressures on demand of professionals from countries of the south to sustain economic growth. Health systems in countries of the south are facing increasing rate of migration of health workforce including physicians and allied personnel. Such situation led WHO to promote the code of ethical recruitment of international health professionals. The optional nature of the code did not allow its wider implementation. The brain drain of scare resources represents a real challenge for health systems in several countries including Tunisia. Since 2011, the pace of migration of Tunisian health professionals and particularly physicians have increased because of worsening working conditions, limited career path and uncertainty about the future. Europe and particularly France, Canada, Germany and Gulf states constitute the main destination of Tunisian migrants partly explained by cultural aspects including Arabic and French languages and similarity of Francophone model of medical education. The present qualitative study including focus group discussions with main stake holders aims at: Measuring the brain drain of Tunisian health professionalsAssessing the impact of brain drain on the Tunisian health systemSharing initiatives aimed at retaining health professionals in public sector and inside the country.Learning lessons from other countries on working models for well organized and mutually beneficial migration of health professionals.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".