Assessing the Socioeconomic Determinants of Malaria Prevalence Among Students in Anyigba
Bibliographic record
Abstract
Background: Malaria remains a major public health concern in Nigeria, with university students in endemic areas particularly vulnerable due to varying socioeconomic conditions. This study investigates the influence of socioeconomic factors on malaria prevalence among students of Kogi State University, Anyigba, Nigeria.Aims: The study aimed at assessing the socioeconomic determinants of malaria prevalence among students in Anyigba, Kogi State. To also examine the relationship between students' socioeconomic status and malaria incidence. To identify key environmental and behavioral factors contributing to malaria exposure, and to recommend policy and health interventions tailored to student populations in malaria-endemic areas.Methods: A cross-sectional descriptive design was adopted. Stratified random sampling was used to select participants across variables such as age, gender, income, and housing types. Data were collected through structured questionnaires and analyzed using logistic regression to determine significant predictors of malaria prevalence.Results: The findings revealed a high malaria prevalence among low-income students and those living in shared accommodations. Key determinants of malaria incidence included low income, poor housing conditions, and limited access to healthcare services. Notably, students with poor access to healthcare had a malaria prevalence of 68%, compared to 33% among those with better access.Conclusion: Socioeconomic factors significantly influence malaria prevalence in the student population. Financial constraints, inadequate housing, and poor healthcare access increase vulnerability to infection. An integrated malaria control approach is recommended, including university-led awareness campaigns, improved sanitation in student accommodations, and enhanced collaboration with local healthcare providers.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".