Managed Migration: The Caribbean Approach to Addressing Nursing Services Capacity
Bibliographic record
Abstract
OBJECTIVE: To (1) provide a contextual analysis of the Caribbean region with respect to forces shaping the current and emerging nursing workforce picture in the region; (2) discuss country-specific case(s) within the Caribbean; and (3) describe the Managed Migration Program as a potential framework for addressing regional and global nurse migration issues. PRINCIPAL FINDINGS: The Caribbean is in the midst of a crisis of shortages of nurses with an average vacancy rate of 42 percent. Low pay, poor career prospects, and lack of education opportunities are among the reasons nurses resign. Many of these nurses look outside the region for job opportunities in the United Kingdom, Canada, the United States, and other countries. Compounding the situation is the lack of resources to train nurses to fill the vacancies. The Managed Migration Program of the Caribbean is a multilateral, cross-sector, multi-interventional, long-term strategy for developing and maintaining an adequate supply of nurses for the region. CONCLUSIONS: The Managed Migration Program of the Caribbean has made progress in establishing regional support for addressing the nursing shortage crisis and developing a number of interesting initiatives such as training for export and temporary migration. Recommendations to move the Managed Migration Program of the Caribbean forward focus on advocacy, integration of the program into regional policy decisions, and integration of the program with regional health programming.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".