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Socio-Demographic Determinants of Prompt Malaria Treatment for Children Under 5 Years by Caregivers in Nigeria

2025· article· en· W4408918531 on OpenAlexaff

Bibliographic record

VenueTexila international journal of public health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health and Epidemiology
Canadian institutionsGovernment of Alberta
Fundersnot available
KeywordsMalariaEnvironmental healthMedicineGeographySocioeconomicsSociologyImmunology

Abstract

fetched live from OpenAlex

Malaria is a major cause of morbidity and mortality in Nigeria. Access to prompt and effective treatment of malaria is at the core of the prevention of deaths in under 5 children in Nigeria. This study investigates the socio-demographic determinants of prompt response behavior of caregivers to malaria among children under 5 years in Nigeria. The analysis is based on 14,6471 women aged 15- 49 years from the most recent national population-based survey (Nigeria Malaria Indicator Survey 2021) using chi-square and logistic regression methods. The study is based on caregivers whose children had malaria at least 2 weeks before the survey across the 36 states in Nigeria including the Federal Capital Territory (FCT). Knowledge of fever, educational attainment of caregivers and family wealth are all significant determinants of prompt malaria treatment for children under 5 years in Nigeria. Respondents with adequate knowledge of malaria signs and symptoms were 1.5 times more likely to seek prompt treatment, those who have at least primary education were 1.7 times more likely to seek prompt treatment while the higher the family wealth, the more likely it is for a caregiver to seek prompt treatment for malaria for children under 5 years. Despite the high knowledge of malaria among caregivers, there is still low timely treatment response for children under 5 years. Therefore, programs should focus on increasing awareness and benefits of prompt care-seeking among caregivers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.471
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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