What is the financial incentive to immigrate? An analysis of salary disparities between health workers working in the Caribbean and popular destination countries
Bibliographic record
Abstract
BACKGROUND: The continuous migration of Human Resources for Health (HRH) compromises the quality of health services in the developing supplying countries. The ability to increase earnings potentially serves as a strong motivator for HRH to migrate abroad. This study adds to limited available literature on HRH salaries within the Caribbean region and establishes the wage gap between selected Caribbean and popular destination countries. METHODS: Salaries are reported for registered nurses, medical doctors and specialists. Within these cadres, experience is incorporated at three different levels. Earnings are compared using purchasing power parity (PPP) exchange rates allowing for cost of living adjusted salary differentials, awarded to different levels of work experience for the chosen health cadres in the selected Caribbean countries (Jamaica, Dominica, St Lucia and Grenada) and the three destination countries (United States, United Kingdom and Canada). RESULTS: Registered nurses in the destination countries, across all experience levels, have greater spending power compared to their Caribbean counterparts. Recently qualified registered nurses earn substantially more in the UK (86.4%), US (214.2%) and Canada (182.5% more). The highest PPP salary ($) gap amongst more experienced nurses (5-10 years) is found within the US, with a gap of 163.9%. PPP salary gaps amongst medical doctors were pronounced, with experienced cadres (10-20 years of experience) in the US earning 316.3% more than their Caribbean counterparts, whilst UK doctors (183.5%) and Canadian doctors (251.3%) also earning significantly more. Large salary differentials remained for medical specialists and consultants. US specialist salaries were 540.4% higher than their Caribbean based counterparts, whilst UK and Canadian specialists earned 95.2 and 181.6% more respectively. CONCLUSION: The PPP adjusted HRH salaries in the three destination countries are superior to those of comparable HRH working in the Caribbean countries selected. The extent of the salary gaps vary according to country and the health cadre under examination, but remain considerable even for newly qualified HRH. The financial incentive to migrate for HRH trained and working in the Caribbean region remains strong, with governments having to consider earning potential abroad when formulating policies and strategies aimed at retaining health professionals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".