“We are the ones who will have to make the change”: Cuban health cooperation and the integration of Cuban medical graduates into practice in the Pacific
Bibliographic record
Abstract
BACKGROUND: This paper responds to Asante et al. (in Hum Resour Health, 2014), providing an updated picture of the impacts of Cuban medical training in the Pacific region based on research carried out in 2019-2021, which focused on the experiences of Pacific Island doctors trained in Cuba and their integration into practice in their home countries. METHODS: The research focussed on two case studies-Solomon Islands and Kiribati. Study methods for this research included multi-sited ethnographic methods and semi-structured interviews as well as qualitative analysis of policy documents, reports, and media sources. RESULTS: The Cuban health assistance programme has had a significant impact on the medical workforce in the Pacific region increasing the number of doctors employed by Pacific Ministries of Health between 2012 and 2019. Qualitatively, there have been some notable improvements in the medical workforce and health delivery over this period. However, the integration of the Cuban-trained doctors into practise has been challenging, with criticisms of their clinical, procedural and communication skills, and the need for the rapid development of bridging and internship training programmes (ITPs) which were inadequately planned for at the outset of the programme. CONCLUSIONS: The Cuban programme in the Pacific is an important model of development assistance for health in the region. While Cuba's offer of scholarships was the trigger for a range of positive outcomes, the success of the programme has relied on input from a range of actors including support from other governments and institutions, and much hard work by the graduates themselves, often in the face of considerable criticism. Key impacts of the programme to date include the raw increase in the number of doctors and the development of the ITPs and career pathways for the graduates, although this has also led to the reorientation of Cuban graduates from preventative to curative health. There is considerable potential for these graduates to contribute to improved health outcomes across the region, particularly if their primary and preventative health care skills are utilised.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".