Determinants of Use of Insecticide Treated Bednets Among Caregivers of Under Five Children in an Urban Local Government Area of Osun State, South-Western Nigeria
Bibliographic record
Abstract
In Sub Sahara Africa, the use of Insecticide Treated Nets (ITNs) is one of many strategies of Roll Back Malaria (RBM) initiatives to reduce malaria burden. This study therefore assessed the current use of insecticide treated nets and the determinants of its use among the caregivers of under five children in an urban local government area in Osun state, Nigeria. The study utilised a cross-sectional design among caregivers of under- five children selected from households by multistage sampling technique. The study collected quantitative data using pretested semi structured, interviewer administered questionnaire while factors that determine the current use of ITN were identified using multi linear logistic regression. The study revealed that 54.4% caregivers of under five children were aware of ITNs as one of the malaria preventive measures, 49.1% had good knowledge of ITN and 38% agreed with the use of ITNs. Thirty four percent had access to ITNs, 32.3% owned at least one ITN with 30.3% reported been given free in the health care facilities. Thirty three percent had ever used and the foremost reasons for non-use are not readily available and expensive. Only 18.5% currently used ITNs and challenges faced were not easy to treat, difficult to set up and no place to keep it. Marital status, knowledge of ITN, attitude towards ITN, ownership of ITN and free ITN were factors that determined the use of ITNs amongst the respondents. There is a need to ensure intensive awareness on ITNs through campaigns and embark on its mass distribution to the public to enhance use.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".