Access and Adequate Utilization of Malaria Control Interventions among Women of Childbearing Age from 15 to 49 years in Badbaado IDPs Settlements, Mogadishu, Somalia
Bibliographic record
Abstract
Background: Although there is limited national data and statistics on the burden of malaria in Somalia, it is considered a major public health problem in the country. children below 5 years, pregnant, lactating women and non-immune migrants carry most of the disease burden. the world malaria report 2020 estimated that there were around 759,000 cases and 1,942 deaths in Somalia in 2019. Aim of the study: The purpose of this study is to explore the results of a rapid assessment of the extent of current access and adequate utilization of malaria control interventions among women of childbearing age from 15 to 49 years in Badbaado IDPs Settlements, Dharkenley District, Mogadishu, Somalia. Method: This study applied a non-probability purposive sampling strategy for recruiting study participants. A total of 150 women aged 15 to 49 years old were selected, and semi-structured questionnaires were the main data collection methods. The data was analyzed using SPSS version 23 and used a P-value of 95% to assess associations between variables with ≤0.05 regarded as a statistically significant. Results: The incidence of malaria among respondents was 59 cases (39.3%), of which 39 (66.1%) were mothers followed by 17 cases (28.8%) of children under the age of five years. The vast majority of 51 (63.0%) of the respondents who seek treatment confirmed that the distance from the health facility to their residence is about three kilometers or further. The majority of 39 (66.1%) of the respondents who were infected with malaria did not take the malaria medicine, while non-availability and/or non-affordability of the prescribed medicines in the clinics was the reason for not taking the medicine. Most of the respondents, 140 out of 150 of the study participants (93.3%), confirmed that they did not get any malarial services in their internally displaced persons IDPs settlements. Almost all of the respondents’ household members 147 (98%) did not own insecticide-treated bed nets (ITNs), reasoning that due to the lack of distribution of ITNs and the unaffordability of their costs. Conclusion and Recommendation: The study revealed a high incidence of malaria cases. However, this study recommends the government and other stakeholders should provide funding to establish IDPs settlements clinics and increase mobile teams to provide adequate and accessible public health services to combat malaria in these vulnerable populations.
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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 | 0.001 | 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) | 0.002 | 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".