Knowledge, perception and practices about malaria, climate change, livelihoods and food security among rural communities of central Tanzania
Bibliographic record
Abstract
BACKGROUND: Understanding the interactions between malaria and agriculture in Tanzania is of particular significance when considering that they are the major sources of illness and livelihoods. The objective of this study was to determine knowledge, perceptions and practices as regards to malaria, climate change, livelihoods and food insecurity in a rural farming community in central Tanzania. METHODS: Using a cross-sectional design, heads of households were interviewed on their knowledge and perceptions on malaria transmission, symptoms and prevention and knowledge and practices as regards to climate change and food security. RESULTS: A total of 399 individuals (mean age = 39.8 ± 15.5 years) were interviewed. Most (62.41%) of them had attained primary school education and majority (91.23%) were involved in crop farming activities. Nearly all (94.7%) knew that malaria is acquired through a mosquito bite. Three quarters (73%) reported that most people get sick from malaria during the rainy season. About 50% of the respondents felt that malaria had decreased during the last 10 years. The household coverage of insecticide treated mosquito nets (ITN) was high (95.5%). Ninety-six percent reported to have slept under a mosquito net the previous night. Only one in four understood the official Kiswahili term (Mabadiliko ya Tabia Nchi) for climate change. However, there was a general understanding that the rain patterns have changed in the past 10 years. Sixty-two percent believed that the temperature has increased during the same period. Three quarters of the respondents reported that they had no sufficient production from their own farms to guarantee food security in their household for the year. Three quarters (73.0%) reported to having food shortages in the past five years. About half said they most often experienced severe food shortage during the rainy season. CONCLUSION: Farming communities in Kilosa District have little knowledge on climate change and its impact on malaria burden. Food insecurity is common and community-based strategies to mitigate this need to be established. The findings call for an integrated control of malaria and food insecurity interventions.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".