‘WE CAN’T GET WORMS FROM COW DUNG’: REPORTED KNOWLEDGE OF PARASITISM AMONG PASTORALIST YOUTH ATTENDING SECONDARY SCHOOL IN THE NGORONGORO CONSERVATION AREA, TANZANIA
Bibliographic record
Abstract
Records at the Endulen Hospital in the Ngorongoro Conservation Area (NCA), Tanzania, reveal that soil-transmitted helminth infections and protozoa are consistently in the top ten diagnoses for Maasai pastoralists, indicating a significant public health concern. Nevertheless, Maasai pastoralist adaptations to life in close proximity to livestock and to unreliable access to water raise important questions about experiences of, and resiliency to, parasitic infections. Though these infections are particularly prevalent among youth in low- and middle-income countries (LMIC), a focus on resiliency highlights local capacity to recover from and prevent illness. For instance, how is human parasitism perceived and experienced among communities displaying behaviours that studies have associated with transmission of diarrhoeal diseases, such as open defecation? Among these communities, how is parasitism seen to impact the health and development of children? And, what resources are available to endure or mitigate this heavy disease burden among affected communities? This study draws on formative research carried out in May 2014 in anticipation of an innovative school-based and youth-driven water, sanitation and hygiene education intervention rolled out in two boarding schools in the NCA in subsequent months. The initiative is grounded in a One Health approach to health promotion, drawing on partnerships in medicine, public health and veterinary medicine to appreciate the unique interactions between humans, animals and the environment that shape well-being among pastoralist communities. Qualitative data generated through group discussions with secondary school youth (n=60), Maasai teachers (n=6) and a women's group (n=8) in the NCA convey existing knowledge of the prevalence, prevention and treatment of human parasitism. An underlying principle of the larger initiative is to engage youth as creative agents of change in developing and sustaining locally relevant health promotion strategies. Findings highlight practical knowledge around certain 'neglected tropical diseases', namely helminths, among pastoralist communities in the NCA, in turn feeding into the development of the science fair and related interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".