The Migrant Care Work Regime and the Making of the Constant‐Care Worker Among Indian Immigrant Nurses
Bibliographic record
Abstract
ABSTRACT Indian nurses coming to the U.S. on H‐1B visas that tie them to employers are often the breadwinner for spouses and other family members on dependant visas, where the dependant spouses do not have the ability to work due to visa regulations, thus upsetting complex gender dynamics in the household. In this paper, we investigate how Indian immigrant nurses in the U.S. navigate their status as the primary migrant and, family breadwinner with the incessant feminizing discourse of immigrant nurses as selfless healers. The paper is based on in‐depth interviews with 30 Indian migrant nurses and their husbands, 10 immigration experts such as lawyers, legislators and activists, 3 years of ethnography in nursing conferences that Indian nurses in the U.S. attended, and in the homes and churches that the nurses and their families attended. Using the construct of “the caring self” our analysis shows that the nurses are continuously subjected to the discourse of nursing work as healing work in formal and informal contexts such as, by recruitment agencies, at nursing conferences, by superiors and peers at work, in their churches and at home. The internalization of the discourse of immigrant nurses as healers often prevent the nurses from processing and verbalizing their gendered and racialized experiences at work, demands of caregiving at home and the community at large. Nursing work as healing work becomes their path toward making of the caring self in the new country. As such, we argue that immigrant nurses operate in a “migrant care work regime” which is bolstered by the intersections of migration policies and a feminized and racialized discourse of migrant nursing work, creating the trope of the constant care worker that shapes the material realities of nurses as immigrant women care workers in the U.S.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".