Care and Global Migration in the Nursing Profession: a north Indian perspective
Bibliographic record
Abstract
Globalisation, supply–demand dynamics, uneven development, enhanced connectivity including the better flow of information, communication and the reduced cost of travel have encouraged the global integration of nursing labour markets. Developed regions of the world have attracted internationally educated nurses (IENs) because of growing healthcare needs. India, along with the Philippines, has become a key supplier of nurses in the global economy. Traditionally the supply of nurses was heavily regionalised in south India, especially Kerala, but of late Punjab, in north India, has played an increasing role in nurse training and migration as the profession has become more respected and more international. This paper uses survey and interview data to detail the recent interest in nursing as a channel for independent female international migration from Punjab, and to examine how migratory ambitions have developed over the last decade in parallel with the changing status of nursing as an internationally respected profession. We identify growing interest in international migration for nursing students and their increased intention to pursue employment opportunities in Australia and New Zealand. This research highlights how nursing and care migration are increasingly structured by international circuits of training and employment, and how such circuits alter migrant and occupational geographies on the ground in sending regions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".