Providing Probiotics to Sub-Saharan Africa: Ethical Principles to Consider
Bibliographic record
Abstract
The United Nations Food and Agriculture Organization and World Health Organization in 2001, made a clarion call for efforts to be made to make probiotic products more widely available, especially for relief work and populations at high risk of morbidity and mortality. This strong and direct request to governments, funding agencies, corporate pharmaceutical and food industries has so far not had any impact in sub-Saharan Africa, where people are mired in poverty and stricken with gastro-intestinal and an escalating epidemic of sexually transmitted diseases including HIV/AIDS.The ability of certain probiotic strains to prevent and treat some gut and urogenital conditions, and the relative low cost and practical means by which this can be achieved, provides a potential addition to the armamentarium of methods to lower the impact of disease and enhance the quality of life of people in this part of the world.Companies selling reliable probiotic products have not yet entered sub-Saharan Africa, perhaps due to logistical reasons and low pricing. Nevertheless, in order to make a significant impact on the health and wellbeing of this large population, ways must be found to provide access to this technology. A community kitchen project in Tanzania, initiated by our group, is one way of taking the concept to the grass roots.
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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 | 0.079 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.023 | 0.043 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".