Sociocultural Factors Influencing Human Streptococcus suis Disease in Southeast Asia
Bibliographic record
Abstract
The public health systems of Southeast Asian countries are financially challenged by a comparatively higher incidence of human S. suis infections than other geographical areas. Efforts to improve practices in production settings, including improved meat inspection regulations, prevention of the slaughtering of non-healthy pigs, and enhanced hygiene practices at processing facilities, along with improvements in the pork supply chain, all appear promising for reducing food cross-contamination with S. suis. However, opportunities for intervention at the societal level are also needed to effect changes, as population behaviors such as the consumption of raw pork, blood, and offal products are important contributors to the increased incidence of human S. suis disease in Southeast Asia. A plethora of factors are associated with the consumption of these high-risk dishes, including traditional culture and knowledge, shared beliefs, socio-economic level, and personal attitudes associated with gender and/or marital status. Education and intervention in behavioral attitudes that are sensible to cultural practices and traditions may provide additional means to reduce the burden of S. suis human disease in Southeast Asia.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".