Emigration and job security: An analysis of workforce trends for Spanish‐trained nurses (2010–2015)
Bibliographic record
Abstract
AIM: To analyse the relationship between Spanish nurses' intention to migrate and job security. BACKGROUND: Nursing emigration from Spain increased dramatically between 2010 and 2013. By 2015, emigration had returned to 2010 levels. METHODS: Single embedded case study. We examined publicly available statistics to test for a relationship between job security and applications by Spanish nurses to have credentials recognized for emigration purposes. RESULTS: Between 2010 and 2015, job security worsened, with poor access to the profession for new graduates, increased rate of professional dropout, increased nursing jobseekers and falling numbers of permanent contracts. CONCLUSIONS: The number of accreditation applications in Spain in 2010 and 2015 was very similar, but job security worsened on a number of fronts. The distribution of work through part-time contracts aided retention. IMPLICATIONS FOR NURSING MANAGEMENT: Policymakers and health care administrators can benefit from understanding the relationship between mobility, workforce planning and the availability of full-time, part-time and short-term contract work in order to design nursing retention programmes and ensure the sustainability of the health care system.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".