Knowledge, Attitudes, and Practices of Parents Regarding Malaria Chemoprevention for Children in ILORIN, KWARA State.
Bibliographic record
Abstract
Introduction: Malaria chemoprevention has been adopted in Nigeria as one of the preventive strategies aimed at reducing the burden of malaria, but the effectiveness of these interventions is intrinsically linked to the knowledge, attitudes, and practices of parents and caregivers regarding this malaria prevention strategy. This study aims to comprehensively assess the knowledge, attitudes, and practices of parents concerning this preventive method, thereby informing targeted interventions to improve uptake and adherence to malaria prevention strategies. Methods: A hospital-based, descriptive cross-sectional study design was employed using a convenient sampling technique to administer a pretested, semi-structured questionnaire to the sample population of caregivers of under 5 children at the outpatient department of the selected hospital. Results: The results indicate that although 57.2% of caregivers had heard of SMC, only 28.4% reported their child had taken it, revealing a disparity between awareness and actual uptake. Key barriers to SMC adoption included poverty, difficult access to healthcare services, and concerns over drug side effects. The study also revealed a significant relationship between caregivers’ knowledge of SMC and its uptake (p = 0.005). In addition, factors influencing caregivers’ decision to allow SMC treatment included perceived effectiveness, trust in healthcare providers, and accessibility of healthcare. Despite a generally positive attitude toward malaria prevention, existing knowledge gaps continue to hinder the effective implementation of preventive measures. Conclusion: The study concluded that enhancing health education and awareness, improving healthcare service accessibility, and addressing socioeconomic challenges are vital 1. Ajayi, M.Y., Emeto, D.C. 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(2019). Usefulness of Polymerase Chain Reaction in the diagnosis of asymptomatic malaria among school age children in Ilorin, Nigeria. Pan African Journal of Life Sciences, 1, 39-45. 8. Kio, J. O., Agbede, C. O., Olayinka, F. E., Omeonu, P. E., & Dire-Arimoyo, Y. (2016). Knowledge, attitudes, and practices of mothers of under-five regarding prevention of malaria in children: Evidence from Ogun State, Nigeria. IOSR Journal of Humanities and Social Science (IOSR-JHSS), 21(8), 1-7. https://doi.org/10.9790/0837-2108080107 9. Singh R, Godson II, Singh S, Singh RB, Isyaku NT, Ebere UV. High prevalence of asymptomatic malaria in apparently healthy schoolchildren in Aliero, Kebbi state, Nigeria. J Vector Borne Dis. 2014 Jun;51(2):128-32 10. Kimbi HK, Nkesa SB, Ndamukong-Nyanga JL, Sumbele IU, Atashili J, Atanga MB. Knowledge and perceptions towards malaria prevention among vulnerable groups in the Buea Health District, Cameroon. 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Front Public Health. 2023 15. Diarra, Y. et al. (2025) ‘Caregiver acceptability of seasonal malaria chemoprevention in two districts in the Upper West region, Ghana: a cross-sectional study’, Malaria journal, 24(1), p. 14. Available at: https://doi.org/10.1186/s12936-024-05169-6 16. Ibinaiye, T. et al. (2023) ‘Predictors of caregiver adherence to administration of amodiaquine during delivery of seasonal malaria chemoprevention in Nigeria, Burkina Faso, Chad, and Togo’, Malaria Journal, 22(1). Available at: https://doi.org/10.1186/s12936-023- 04576-5. 17. Dicko, I., Coulibaly, Y.I., Sangare, M., Sarfo, B. and Nortey, P.A. (2020) ‘Non-compliance to mass drug administration associated with the low perception of the community members about their susceptibility to lymphatic filariasis in Ankobra, Ghana’, Infectious Disorders - Drug Targets, 20, pp. 167–174. 18. Antwi, G.D., Bates, L.A., King, R., Mahama, P.R., Tagbor, H., Cairns, M. et al. (2016) ‘Facilitators and barriers to uptake of an extended seasonal malaria chemoprevention programme in Ghana: a qualitative study of caregivers and community health workers’, PLoS ONE, 11, e0166951. 19. Ba, E.H., Pitt, C., Dial, Y., Faye, S.L., Cairns, M., Faye, E. et al. (2018) ‘Implementation, coverage and equity of large-scale door-to-door delivery of seasonal malaria chemoprevention (SMC) to children under 10 in Senegal’, Scientific Reports, 8, p. 5489. 20. Ngasala, B. et al. (2023) ‘Malaria knowledge, attitude, and practice among communities involved in a seasonal malaria chemoprevention study in Nanyumbu and Masasi districts, Tanzania’, Frontiers in Public Health [Preprint]. Available at: https://doi.org/10.3389/ fpubh.2023.976354.
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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 | 0.000 |
| 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) | 0.001 | 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".