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Record W2520011294 · doi:10.1136/jech-2016-208064.33

OP33 Contextual Factors Associated with Health Care Service Utilisation for Children in Nigeria: A Multilevel Analysis

2016· article· en· W2520011294 on OpenAlex
ST Adedokun, VT Adekanmbi, Olalekan A. Uthman, Richard Lilford

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOral Presentations · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsCluster samplingQuarter (Canadian coin)Logistic regressionMultilevel modelCluster (spacecraft)Environmental healthHealth careMultistage samplingPopulationSystematic samplingRegression analysisMedicineGeographyStatisticsComputer scienceEconomic growth

Abstract

fetched live from OpenAlex

<h3>Background</h3> The leading causes of high under-five mortality in Nigeria are infectious diseases which could be easily prevented and treated through health care services utilisation. There is poor utilisation of these services and most studies examining its determinants have focused on individual factors. The objective of this study is to examine the independent contribution of individual-, community- and state-level factors to health care service utilisation for children in Nigeria. <h3>Methods</h3> The study was based on secondary analyses of cross-sectional population-based data from the 2013 Nigeria Demographic and Health Survey. The survey used a three-stage cluster sampling technique. The first stage involved selecting 896 clusters with a probability proportional to the size; the size being the number of households in the cluster. The second stage involved the systematic sampling of households from the selected clusters. The third stage involved the distribution of the households in each state proportionately among its urban and rural areas. A total of 40,680 households were finally sampled with 16,740 and 23,940 from urban and rural areas respectively. Data were collected by visiting households and administering questionnaires. Multilevel logistic regression models were applied to the data on 31,482 under-five children who used or did not use health care service when they were sick (level 1), nested within 896 communities (level 2) from 37 districts (level 3). All multilevel modelling were performed using MLwiN calling Stata statistical software from windows version 14. <h3>Results</h3> About one-quarter of the mothers were between 15 and 24 years old and almost half of them did not have formal education (47%). Close to 67% of the children lived in the rural area. In the fully adjusted model, mothers with higher education attainment (OR = 1.66, 95% CI 1.37–1.95), from richer households (OR = 1.32, 95% CI 1.04–1.63), with access to media (radio, television or magazine), and living in ethnic diverse communities (OR = 1.04, 95% CI 1.01–1.07) were significantly more likely to have used healthcare services for acute childhood illnesses. <h3>Conclusion</h3> Our study revealed that utilisation of healthcare service for acute childhood illnesses was influenced by not only maternal factors but also various community- and state-level factors, suggesting that public health strategies should recognise these complex web of individual composition and contextual composition factors to guide provision of healthcare services. The study was limited due to inability to measure the impact of residential changes over time. Further research should consider longitudinal study.

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.

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 score0.026
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.046
GPT teacher head0.351
Teacher spread0.304 · 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