Migration of Latin American nurses to Spain 2006–2016: a case study
Bibliographic record
Abstract
AIM: To examine the migration of nurses from Latin America to Spain over the period from 2006 to 2016. BACKGROUND: This study examines the impact of the 2008 global economic crisis on migration flows of nurses to Spain from its major source countries of Latin America. METHODS: Using an exploratory case study, we present original data provided by the Ministry of Education, Culture and Sport of the Government of Spain upon request on applications and success rates for credential recognition of nurses intending to immigrate to Spain, with an extended analysis of Latin American applications which account for the 70% of skilled worker migration to Spain. RESULTS: Successful applications for credential recognition of overseas nursing qualifications plummeted from a peak of 1384 in 2007 to 55 in 2016. Migration intentionality also decreased but has undergone a slight increase in recent years. DISCUSSION/CONCLUSION: We found that the economic crisis effectively closed the door to internationally educated nurses to work as nurses in Spain. Moreover, the denial of official recognition of nursing credentials appears to be unaffected by the existence of bilateral trade and mobility agreements between Spain and source countries. We conclude that the level of nursing migration to Spain is a sensitive indicator of domestic labour market conditions. IMPLICATIONS FOR HEALTH POLICY: Despite the lack of any transparent policy on the credential approvals, in practice the government is limiting access to the nursing labour market by overseas education nurses. We urge that attention be paid by health human resource planners on the intersection between labour market and migration trends to support a transparent and data-informed discussion by all stakeholders on the current state of the nursing labour market in Spain and its future needs.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".