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Record W1819599779 · doi:10.25336/p6fs67

Association of Social Class with Malaria Prevalence Among Household Heads in Ghana

2006· article· en· W1819599779 on OpenAlexaffvenue
Kwame Boadu, Frank Trovato

Bibliographic record

VenueCanadian Studies in Population · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMarital statusSocial classResidenceSocioeconomicsMalariaLogistic regressionHousehold incomeDemographyPovertyEnvironmental healthGeographyMedicinePopulationSociologyEconomicsEconomic growth

Abstract

fetched live from OpenAlex

This is an exploratory study that investigates the association of social class with malaria prevalence among household heads in Ghana. Data utilized is taken from the 1997 Core Welfare Indicators Questionnaire (CWIQ) survey of Ghana. The survey collected information on households covering a variety of topics including education, health, employment, household assets, household amenities, poverty predictors, and child anthropometry. A total of 14,514 households were interviewed, comprising 63 percent rural household heads and 37 percent urban household heads. The research method employed in this study involves the construction of a composite index of social class from six indicators namely, education, dwelling ownership, heads of cattle, modern household items, main source of cooking fuel and type of toilet facility. Logistic regression was applied in examining the association between social class and the dependent variable, prevalence of malaria. Marital status and personal hygiene were examined together with social class as the predictor variables, while sex, age, place of residence and ecological zone were introduced as control variables. The study revealed that there was no direct association between social class and the prevalence of malaria among household heads in Ghana; rather, marital status served as a mediating factor.

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)0.001
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 score0.322
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.278
Teacher spread0.257 · 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

Citations8
Published2006
Admission routes2
Has abstractyes

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