Brain drain and health professionals
Bibliographic record
Abstract
# Is state ownership of health professionals' intellect being proposed? {#article-title-2} EDITOR—In their editorial on the migration of medical professionals Pang et al suggest that “Just as intellectual property rights need to be discussed by developed and developing countries together, so also should the preservation of the intellectual property of a nation, embodied in its health professionals, be addressed by international organisations.”1 Does this mean that the state or some international organisation has a financial claim on a person's intellect? It is one thing to require public service in exchange for education as long as both parties agree beforehand. It is quite another to extort service or money from people who have paid for their own education; this type of action would be justified only at a time of national calamity, such as a world war. Because we so value liberty, most Americans would find this view utterly preposterous. Hopefully many British people will as well. 1. ↵1. Pang T, 2. Lansang MA, 3. Haines A .Brain drain and health professionals.BMJ2002; 324:499–500. (2 March.) [OpenUrl][1][FREE Full Text][2] # Brain drain disseminates skill and advances science {#article-title-4} EDITOR—I cannot understand why some people have difficulty understanding the freedom of movement of professionals.1Professionals move from one region to another and from one country to another all the time. It happens everywhere. This phenomenon is nothing new. The people who leave their country have their reasons for going. Einstein left Germany for the United States in the 1930s for fear of Nazi persecution. Osler emgrated from Canada to John Hopkins University in the United States and eventually ended up in Oxford, England. In the United States we have a variety of professionals from all over the earth. This phenomenon enriches cultures, disseminates skill and information, and advances science and technology. I … [1]: {openurl}?query=rft.jtitle%253DBMJ%26rft.stitle%253DBMJ%26rft.issn%253D0007-1447%26rft.aulast%253DPang%26rft.auinit1%253DT.%26rft.volume%253D324%26rft.issue%253D7336%26rft.spage%253D499%26rft.epage%253D500%26rft.atitle%253DBrain%2Bdrain%2Band%2Bhealth%2Bprofessionals%26rft_id%253Dinfo%253Adoi%252F10.1136%252Fbmj.324.7336.499%26rft_id%253Dinfo%253Apmid%252F11872536%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=FULL&journalCode=bmj&resid=324/7336/499&atom=%2Fbmj%2F325%2F7357%2F219.atom
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 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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.033 | 0.032 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".