Indigenous environmental indicators for malaria: A district study in Zimbabwe
Bibliographic record
Abstract
This paper discusses indigenous environmental indicators for the occurrence of malaria in ward 11, 15 and 18 of Gwanda district, Zimbabwe. The study was inspired by the successes of use of indigenous knowledge systems in community based early warning systems for natural disasters. To our knowledge, no study has examined the relationship between malaria epidemics and climatic factors in Gwanda district. The aim of the study was to determine the environmental indicators for the occurrence of malaria. Twenty eight key informants from the 3 wards were studied. Questionnaires, focus group discussions and PRA sessions were used to collect data. Content analysis was used to analyse the data. The local name for malaria was 'uqhuqho' literally meaning a fever. The disease is also called, "umkhuhlane wemiyane" and is derived from the association between malaria and mosquitoes. The findings of our study reveal that trends in malaria incidence are perceived to positively correlate with variations in both temperature and rainfall, although factors other than climate seem to play an important role too. Plant phenology and insects are the commonly used indicators in malaria prediction in the study villages. Other indicators for malaria prediction included the perceived noise emanating from mountains, referred to as "roaring of mountains" and certain behaviours exhibited by ostriches. The results of the present study highlight the importance of using climatic information in the analysis of malaria surveillance data, and this knowledge can be integrated into the conventional health system to develop a community based malaria forecasting system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".