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Record W4200585127 · doi:10.1186/s41182-021-00385-1

Health care seeking behaviour for children with acute childhood illnesses and its relating factors in sub-Saharan Africa: evidence from 24 countries

2021· article· en· W4200585127 on OpenAlexaff
Sanni Yaya, Emmanuel Kolawole Odusina, Nicholas Kofi Adjei

Bibliographic record

VenueTropical Medicine and Health · 2021
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of OttawaGlobal Affairs Canada
Fundersnot available
KeywordsMedicinePsychological interventionHealth careLogistic regressionPublic healthDeveloping countryEnvironmental healthOdds ratioChild mortalityOddsDemographyPopulationPsychiatryNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Childhood illnesses and mortality rates have declined over the past years in sub-Saharan African countries; however, under-five mortality is still high in the region. This study investigated the magnitude and factors associated with health care seeking behaviour for children with childhood illnesses in 24 sub-Saharan African countries. METHODS: We used secondary data from Demographic and Health Surveys (DHSs) conducted between 2013 and 2018 across the 24 sub-Saharan African countries. Binary logistic regression models were applied to identify the factors associated with health care seeking behaviour for children with acute childhood illnesses. The results were presented using adjusted odds ratios (aOR) with 95% confidence intervals (CIs). RESULTS: Overall, 45% of children under-5 years with acute childhood illnesses utilized health care facilities. The factors associated with health care seeking behaviour for children with acute illnesses were sex of child, number of living children, education, work status, wealth index, exposure to media and distance to a health facility. CONCLUSIONS: Over half of mothers did not seek appropriate health care for under-five childhood illnesses. Effective health policy interventions are needed to enhance health care seeking behaviour of mothers for childhood illnesses in sub-Saharan African countries.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.108
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.326
Teacher spread0.288 · 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

Citations42
Published2021
Admission routes1
Has abstractyes

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