Assessing the ownership, usage and knowledge of Insecticide Treated Nets (ITNs) in Malaria Prevention in the Hohoe Municipality, Ghana
Bibliographic record
Abstract
INTRODUCTION: Malaria remains one of the top five killer diseases in sub-Saharan Africa (SSA) and its burden is skewed towards pregnant women and children under five. Insecticide Treated Bed-Net (ITN) usage is considered one of the most cost-effective, preventive interventions against malaria. This study sought to assess ownership, usage, effectiveness, knowledge, access and availability of ITNs among mothers with children under five in the Hohoe municipality. METHODS: In August 2010 a cross-sectional survey was carried out in 30 communities, selected using the WHO 30 cluster sampling technique. In the selected communities, mothers/caregivers with children under five years were selected using the snowball method. Data were collected through questionnaires and direct observation of ITN. Descriptive statistics was used to analyse the data collected. RESULTS: A total of 450 mothers/caregivers were interviewed and their mean age was 30 ± 7 years. ITN ownership was 81.3%, and usage was 66.4%. The majority (97.8%) of the mothers/caregivers said ITNs were effective for malaria prevention. Awareness about ITNs was high (98.7%) and the majority (52.9%) had heard about ITNs from Reproductive and Child Health (RCH) Clinic and antenatal care ANC clinic (33.6%). Over 60% of the ITNs were acquired through free distribution at RCH clinics, clinic and home distribution during mass immunization sessions. The majority of the mothers/caregivers (78.6%) knew the signs and symptoms of malaria, what causes malaria (82.2%) and who is most at risk (90%). CONCLUSION: Behaviour change communication strategies on ITN use may need to be further targeted to ensure full use of available ITNs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".