Strengthening integrated research and capacity development within the Caribbean region
Bibliographic record
Abstract
BACKGROUND: The Caribbean region, like other developing regions of the world, faces significant challenges in conducting research, especially in the context of limited resource capacities and capabilities. Further, due to its diverse and multiple island states, research capacity is scattered and unevenly spread within the region. The Caribbean EcoHealth Programme (CEHP) is a research program that is structured to improve the capacity and capability of health professionals in the Caribbean region to respond in integrative and innovative ways to on-going and emerging environmental health challenges by means of multi-sectoral interventions. METHODS: Core parts of the CEHP's mission are to (1) conduct collaborative research in areas that the region has identified as critical; (2) build and strengthening integrated approaches to research; and (3) develop and enhance basic research capacity within the Caribbean region.Fundamental to the success of the CEHP's human and resource development mission has been its use of the Atlantis Mobile Laboratory (AML). The AML has allowed the CEHP program to move throughout the Caribbean and be able to respond to calls for specific research and capacity building opportunities. RESULTS: The CEHP's five main research projects have generated the following results: (1) the Persistent Organic Pollutants (POPs) study has evaluated human exposures to POPs, heavy metals, pesticides, and zoonotic infections; (2) the Burden of Illness (BOI) studies have developed protocols for the testing of foodborne microorganisms, strengthen laboratory analytical capabilities, and determined the prevalence and incidence of food-borne illness; (3) the Rainwater Harvesting (RWH) study has evaluated the microbial and chemical quality of rainwater harvesting systems; (4) the Ecotoxicology Water (ETW) studies have provided much needed data on the quality of recreational and drinking water supplies, and (5) the Food Safety Training Program has developed Diploma and M.Sc Agri-Food Safety and Quality Assurance programmes. CONCLUSIONS: The CEHP program provides a successful example of how a collaborative instead of researcher driven research agenda can lead to not only the generation of needed information, but also leave within the region where the research has been carried out the capacity and capabilities to continue to do so independent of outside interventions.
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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 | 0.182 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.011 | 0.065 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".