Emigration of Nurses from the Caribbean: the Case of Trinidad and Tobago
Bibliographic record
Abstract
This paper examines the emigration of nurses from the Caribbean SIDS over the last 50 years, focusing on the situation in Trinidad and Tobago. It makes an attempt to assess the scope of the outflow of nurses by drawing on data available in Trinidad and Tobago and in the two main destination countries, the United States and the United Kingdom. The main push factors triggering this mass exodus along with the various counteracting strategies adopted will be presented. To get the complete picture various pull factors in the receiving countries will be analyzed. Since the emigration of the skilled is not a new phenomenon and its implications on the developing countries are becoming increasingly severe, various efforts have been undertaken at the regional, as well as at the global level to address this imbalance in order to find viable solutions for all parties concerned. The economic implications of the emigration of health professionals will be studied using a model currently developed by the World Health Organization (WHO). Based on the findings of this analysis, policy recommendations will be formulated for use as a guideline for concerned policy makers at various national and international levels.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".