Migration of Spanish nurses 2009–2014. Underemployment and surplus production of Spanish nurses and mobility among Spanish registered nurses: A case study
Bibliographic record
Abstract
BACKGROUND: After the financial crisis of 2008, increasing numbers of nurses from Spain are going abroad to work. OBJECTIVES: To examine the health and workforce policy trends in Spain between 2009 and 2014 and to analyze their correlation with the migration of nurses. DESIGN: Single embedded case study. DATA SOURCES: We examined data published by: Health Statistics, Organization for Economic Cooperation and Development (1996 to 2013); Ministry of Education, Culture and Sports (2006 to 2013); Ministry of Employment and Social Security (2009 to 2014); Ministry of Health, Social Services and Equality (1997 to 2014); and National Institute of Statistics (1976 to 2014). In addition to reviewing the scholarly literature on the topic in Spanish and English, we also examined Spanish mobility laws and European directives. POPULATION: We used the Organization for Economic Cooperation and Development definition of "professionally active nurses" which defines practising nurses and other nurses as those for whom their education is a prerequisite for employment as a nurse. Moreover, we used the term "nursing graduate" as defined by Spanish Ministry of Education to describe those who have obtained a recognized qualification in nursing in a given year, the term "registered nurses" is defined by Spanish law as nurses registered in the Nurses Associations and "unemployed nurses" are those without work and registered as seeking employment. RESULTS: A transformation of the Spanish health system has reduced the number of employed nurses per capita since 2010. Moreover, reductions in public spending, labour market reforms and widespread unemployment have affected nurses in two ways: first by increasing the number of applicants per vacancy between 2009 and 2013, and second, by an increase in casual positions. However, despite the poor job market and decreasing job security, the number of registered nurses and nursing graduates in Spain per year has continued to grow, increasing the pressure on the labour market. CONCLUSIONS: Spain is transforming from a stable nursing labour market, to one that is increasingly producing nurses for foreign markets, principally in Europe. With its low birth rate, increased life expectancy and increasing rates of chronic disease, it is critical for Spain to have sufficient nurses now and into the future. It is important that there be continued study of this phenomenon by Spanish policy makers, health service providers and educators in order for Spain to develop health human resources policies that address the health care needs of the Spanish population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".