Factors in the drop in the migration of Spanish-trained nurses: 1999-2007
Bibliographic record
Abstract
AIM: To reveal correlates of the decrease of Spanish nurse migration (1999-2007). BACKGROUND: Nursing outmigration is a concern for countries. Nurse migration from Spain began in the 1990s. INTRODUCTION: From 1999 to 2007, the yearly number of migrations dropped significantly. We ask what social, economic and policy factors could be related to this drop. METHODS: We used publicly available statistics to confirm hypothesis (1) The drop in nursing migration coincided with a drop in nursing unemployment. Then we hypothesized that this coincided with (1a) a decrease in the number of graduates, (1b) an increase in the number of hospitals and/or beds functioning, and/or (1c) an increase in the ratio of part-time contracts. RESULTS: Our analysis confirms hypotheses (1) and (1c) and disconfirms (1a) and (1b). CONCLUSION: The greater availability of part-time contracts seems to have encouraged nurses to remain in Spain. IMPLICATIONS FOR NURSING MANAGEMENT: The strategy to reduce nursing unemployment with more part-time contracts, while temporarily successful in Spain, brings with it major challenges for patient care and the working life of nurses. We suggest that nurse leaders and health policymakers consider proactive policies to adjust the balance between supply and demand without decreasing the quality of available positions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".