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Record W4224305598 · doi:10.3390/foods11091190

Sociocultural Factors Influencing Human Streptococcus suis Disease in Southeast Asia

2022· review· en· W4224305598 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFoods · 2022
Typereview
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsEnvironmental healthHygieneDiseasePopulationConsumption (sociology)Intervention (counseling)SocioeconomicsMedicineBusinessGeographyNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.105
GPT teacher head0.393
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it