Sending State Perspectives on the Global Migration of Indian Nurses
Bibliographic record
Abstract
ABSTRACT India has become one of the key sources for the global nursing labour force. While there is significant research on the experiences of Indian‐trained nurses in destination countries and their motivations to migrate, relatively less work explores the role of the sending state in this policy field, and rarely are subregional differences considered. In this paper, we focus on the policies and processes that are designed to or otherwise affect nurse migration from India. We draw upon three sources of data: (1) a scoping review of published literature on health worker migration in India, (2) a survey of 1736 health workers in India and (3) interviews with 60 key political and organisational representatives across two regions of India—Kerala and Punjab—as well as from the Delhi Capital Region. Our assessment of the role of the Indian sending state in the international migration of nurses reveals the existence of contradictory policies that are regionally differentiated and distinct. These important subnational jurisdictional policies differ in terms of context, competencies and coherence in managing health worker migration and its resulting impacts on health systems. This research highlights the need to understand jurisdictional differences in any analysis of sending state perspectives on international health worker migration to more fully assess its implication for domestic health systems and changing dynamics in international migration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".