Managing Chronic Diseases in the Dominican Republic
Bibliographic record
Abstract
Chronic conditions and diseases, such as heart disease, stroke, and cancer significantly influence health status around the world.1 By 2020, an estimated 75% of deaths will be caused by chronic diseases, with an estimated 80% of these deaths occurring in low to middle-income countries (LMICs).1,2 Furthermore, problems with chronic disease are exacerbated by socioeconomic and geopolitical factors.3 An example of an LMIC burdened with chronic diseases is the Dominican Republic (DR), where non-communicable diseases account for about 70% of deaths each year.4 The DR is located on the island of Hispaniola, which is shared with Haiti. Despite their geographical proximity, the two countries are vastly different with regards to development, with the DR placing over 50 countries above Haiti on the United Nations (UN) Human Development Index.5 This disparity leads to the migration of many Haitian workers to the DR. A large portion of the Haitian migrants work in the sugar industry as laborers. In fact, approximately 85% of sugarcane workers in the DR are from Haiti.6 The sugarcane workers are primarily housed in batey communities. A batey is an underdeveloped village located near sugar cane fields, built by large multi-national sugar production companies. These villages are severely underfunded, which gives rise to a variety of health and quality of life issues. Common conditions found among batey inhabitants include gastroesophageal reflux disease, hypertension, and upper respiratory infections.7 Given that many Haitian residents are paid extremely low wages and have limited access to many social services, the burden of these health problems is amplified greatly. There have been many attempts to address these problems by both local and foreign aid teams. However, many factors complicate the provision of chronic disease management, such as care aversive behavior and difficulties with achieving continuity of care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".