Social Determinants of Infectious Diseases in South Asia
Bibliographic record
Abstract
South Asian countries have developed infectious disease control programs such as routine immunization, vaccination, and the provision of essential drugs which are operating nationwide in cooperation with many local and foreign NGOs. Most South Asian countries have a relatively low prevalence of HIV/AIDS until now, but issues like poverty, food insecurity, illiteracy, poor sanitation, and social stigma around AIDS are widespread and are creating formidable challenges to prevention of further spread of this epidemic. Besides that, resurgence of tuberculosis along with the emergence of the drug resistant (MDR-TB and XDRTB) strains and the coepidemic of TB and HIV are posing ever-growing threats to the underdeveloped healthcare infrastructure. The countries are undergoing an epidemiological transition where the disease burden is gradually shifting to noncommunicable diseases, but the infectious diseases still account for almost half of the total disease burden. Despite this huge burden of infectious diseases in South Asia, which is second only to Africa, there is yet any study on the social determinants of infectious diseases in a local context. This paper examines various issues surrounding the social determinants of infectious diseases in South Asian countries with a special reference to HIV and tuberculosis. And, by doing so, it attempts to provide a framework for formulating more efficient prevention and intervention strategies for the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".