Caribbean nurse migration—a scoping review
Bibliographic record
Abstract
BACKGROUND: The migration of Caribbean nurses, particularly to developed countries such as Canada, the United States, and the United Kingdom, remains a matter of concern for most countries of the region. With nursing vacancy rates averaging 40%, individual countries and the region collectively are challenged to address this issue through the development and implementation of sustainable, feasible strategies. The aim of this scoping review is to examine the amount, type, sources, distribution, and focus of the conceptual and empirical literature on the migration of Caribbean nurses, and to identify gaps in the literature. METHODS: Identified records were selected and reviewed using Arksey and O'Malley's scoping framework. A comprehensive search was conducted of eight electronic databases and the Google search engine. Findings were summarized numerically and thematically, with themes emerging through an iterative, inductive process. RESULTS: Much of the literature included in our study (N = 6, 33%) originated in the United States. Publications steadily increased between 2003 and 2016, and half of them (N = 9) were journal articles. Many (N = 6, 33%) of the records used quantitative methods. The themes identified were as follows: (1) migration patterns and trends; (2) post-migration experiences; (3) past and present, policies, programs, and practices; and (4) consequences of migration to donor countries. More than half (N = 11, 56%) of the literature addressed nurse migration policies, programs, or practices, either solely or in part. Several gaps were identified including the need for evaluation of the effectiveness of current nurse migration management strategies and to study policies, trends, and impacts in understudied Caribbean countries. CONCLUSION: This review demonstrates the need for future research in key areas such as the impact of nurse migration on health systems and population health. The literature tends to focus on Caribbean countries with higher levels of nurse migration. However, data regarding this phenomenon in other Caribbean countries is needed for a more comprehensive understanding of the plight of the Caribbean region and would answer the call from the International Organization for Migration to study policies, trends, and impacts in understudied Caribbean countries.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".