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Record W2135971393

Analysis of Malaria Incidence using Quasi-Poisson Regression Model: Evidence from Obuasi Municipality, Ghana

2015· article· en· W2135971393 on OpenAlex
Alexander Boateng, Maseka Lesaoana, Timotheus Darikwa, Abenet Belete, Hlengani Siweya

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

VenueJournals & Books Hosting (International Knowledge Sharing Platform) · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHIV/AIDS Impact and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsPoisson regressionMalariaIncidence (geometry)Quarter (Canadian coin)Poisson distributionRegression analysisGeographyDemographyEnvironmental healthStatisticsMathematicsMedicinePopulationImmunologySociology
DOInot available

Abstract

fetched live from OpenAlex

Death and economic losses associated with malaria have become a global phenomenon that need urgent attention. To obtain a better understanding of incidence of the disease, a quasi-Poisson regression model has been applied in this study to determine the incidence of malaria in Obuasi Municipality, Ghana. Our results show that the incidence of malaria is more prevalent among individuals within the ages of 20 to 34 years and those above 50 years, as compared to children under 5 years of age. In addition, the study reveals that most incidence of malaria were reported in the last quarter of every year between 2007 and 2010. Keywords: Obuasi Municipality, malaria, risk factors, quasi-Poisson regression model

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.284
GPT teacher head0.386
Teacher spread0.102 · 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