K-8From Émigré Health Workers to Medical Tourists: when migration challenges health systems
Bibliographic record
Abstract
A health system relies upon two groups of people: health workers, and patients. In recent decades both groups have been on the move globally. This is not a new phenomenon, but has both accelerated and shifted with global labour market opportunities and the global hunt for skilled labour in the case of health workers; and private investments in high-end health care in low- and middle-income countries (LMICs) providing lower-cost treatments, one of the key incentives for patients seeking care outside of their own country for uninsured or under-insured services. There is little dispute that the world faces a global shortage of health workers, which is likely to worsen in the years ahead. There is some disagreement on the extent to which health workers’ migratory choices affect health systems in their home countries, but a consistent pattern is that ‘push’ is a more determining element in such decisions than is ‘pull’. What do findings from a recent international comparative study on the causes and consequences of health workers ‘on the move’ imply about national and international efforts to ensure health equitable outcomes in both ‘source’ and ‘destination’ countries? A similar question applies to patient mobilities, and particularly the flow from high- to low- and middle-income countries. Using findings from another set of comparative studies, what policies might countries growing this ‘medical tourism’ sector pursue to ensure health equitable gains for all of their citizens, and to avoid a private-sector led global health care market from crowding out access to services in line with the Universal Health Care target of the Sustainable Development Goals? Finally, what role have neoliberal globalization policies and practices played in both sets of flows, and how might governments increase a countervailing emphasis on health equity within in their foreign policies?
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.050 | 0.006 |
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".