Knowledge, perception and practices about malaria, climate change, livelihoods and food security among rural communities of central Tanzania
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».