Mobility and health sector development in China and India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
China and India are both attempting to create comprehensive healthcare systems in the context of rapid but uneven economic growth and rapidly changing burdens of disease. While in each country the referencing of international policies and work experience abroad have been part of this process, research has yet to examine the kind of knowledge that is exchanged or the various actors involved in knowledge circulation. Based on a study of two sub-national contexts, this article focuses on the role Chinese and Indian health professionals who have studied and worked overseas play in introducing ideas and practices about healthcare provision and health education. We found that experience abroad influenced individuals, institutions, and each society differently and with some contradictory effects. International experience clearly contributed to personal growth and led individuals to support the adoption of new institutional practices, such as more egalitarian relations between doctors and patients and between students and teachers. However, the content of what individuals learned overseas and the mechanisms through which this knowledge was introduced back into homeland settings often reinforced rather than ameliorated institutional hierarchies and social inequalities. While the scope of this research was limited, we suggest that more explicit analysis of the role professional migrants play in transferring ideas and practices within the health sector would be valuable for policymakers and funders seeking to support a more productive interaction between local and global knowledge.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it