Brain Drain: time to apply reverse gear
Bibliographic record
Abstract
The term brain drain is primarily used to indicate migration of skilled workers from one part to the other region of the world for various reasons. Historically, the phenomenon of brain drain is documented when Byzantine emigrants played an important part in renaissance of Europe in dark ages. Brain drain in healthcare sector has been a problem for developing countries. This phenomenon has raised concern worldwide due to its negative impact on healthcare system of developing countries like Pakistan. The major concern related to international migration of healthcare workers was addressed in 1940s when there was a noticeable emigration from Europe to UK and USA. Since then this trend of emigration has become a reality with changing source countries over a period of time. Currently Pakistan, India, Sri Lanka and Bangladesh are the major “donor countries” for UK, USA, Canada and Australia. The primary reasons for this brain drain are good financial packages, chance to work in a good clinical set-up, better quality of life, stable political situation and religious and ethnic drives.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.061 | 0.027 |
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".