Assessment of Knowledge, Attitude, Practice, and Associated Risk Factors of Communities Towards to Malaria Prevention and Control in Adaberga District, Oromia Regional State, Ethiopia
Bibliographic record
Abstract
Background: In Ethiopia, more than three-quarter of the landmass (altitude <2000 mas) of the country is malarious, and about 68 % (>50 million people) of the total population is residing in areas at risk of malaria infection.Pregnant women and children are the most vulnerable groups.Objective: To assess Knowledge, Attitude, Practice and associated risk factors of the community towards to malaria prevention and control in Adaberga district Biyo wagadi kebele.Methods: Cross sectional study design was used to assess knowledge, attitude and practice towards to malaria prevention and control options.The source population was all households of the kebele's.The questionnaires were translated to Afan Oromo.Totally 212 households participated.About 69 (32.5%) of the respondents were between 18-24 years old.Results: This study revealed that 203(95.8%) of the respondents had information about malaria, but 9(4.2%) had no information about malaria.Sign and symptoms raised were Fever 103(48.6%),headache 92(43.4%),vomiting 11(5.2%),muscle pain 17(8%), loss of appetite 23(10.9%).43.9% knew that malaria is transmitted through mosquito bite; where as 34.4% responded that malaria transmitted through mosquito bite and other insects, 9.4% responded that malaria transmission is through drinking contaminated water.Conclusion: This study indicated that most of the respondents knew that fever as signs and symptoms of malaria.The levels of knowledge, attitude and practice of study participants on prevention and control of malaria were high, so the study participants had been regarded as knowledgeable.Associated risk factors were sex, age, educational level, occupation, and religion of respondents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".