Bed net use and associated factors in a rice farming community in Central Kenya
Bibliographic record
Abstract
BACKGROUND: Use of insecticide-treated nets (ITNs) continues to offer potential strategy for malaria prevention in endemic areas. However their effectiveness, sustainability and massive scale up remain a factor of socio-economic and cultural variables of the local community which are indispensable during design and implementation stages. METHODS: An ethnographic household survey was conducted in four study villages which were purposefully selected to represent socio-economic and geographical diversity. In total, 400 households were randomly selected from the four study villages. Quantitative and qualitative information of the respondents were collected by use of semi-structured questionnaires and focus group discussions. RESULTS: Malaria was reported the most frequently occurring disease in the area (93%) and its aetiology was attributed to other non-biomedical causes like stagnant water (16%), and long rains (13%). Factors which significantly caused variation in bed net use were occupant relationship to household head (chi2 = 105.705; df 14; P = 0.000), Age (chi2 = 74.483; df 14; P = 0.000), village (chi2 = 150.325; df 6; P = 0.000), occupation (chi2 = 7.955; df 3; P = 0.047), gender (chi2 = 4.254; df 1; P = 0.039) and education levels of the household head or spouse (chi2 = 33.622; df 6; P = 0.000). The same variables determined access and conditions of bed nets at household level. Protection against mosquito bite (95%) was the main reason cited for using bed nets in most households while protection against malaria came second (54%). Colour, shape and affordability were some of the key potential factors which determined choice, use and acceptance of bed nets in the study area. CONCLUSION: The study highlights potential social and economic variables important for effective and sustainable implementation of bed nets-related programmes in Sub-Saharan Africa.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".