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Record W3174142370 · doi:10.4081/jphia.2021.1006

Chronic diseases of lifestyle risk factor profiles of a South African rural community

2021· article· en· W3174142370 on OpenAlexaff
Ushotanefe Useh

Bibliographic record

VenueJournal of Public Health in Africa · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsHealth Sciences North
FundersNational Research Foundation
KeywordsMedicineFamily historyObesityRisk factorDiabetes mellitusDiseaseSedentary lifestyleGerontologyChronic diseaseEnvironmental healthRural communityCommunity healthFamily medicinePhysical therapyDemographyPublic healthInternal medicine

Abstract

fetched live from OpenAlex

Globally, chronic diseases of lifestyle account for millions of dollars spent annually on health. These diseases share similar risk factors including: physical inactivity, obesity, cigarette smoking, and hypertension among others. This study sought to assess risk factors for chronic diseases of lifestyle of a rural community in South Africa. This study used a survey design with data randomly collected using the WHO STEPS Instrument for Chronic Disease Risk Factor Surveillance from participants who attended routine checks from February to October 2018 from a trained healthcare practitioner. Informed consent was sought from all participants before the administration of the instrument. The research setting was the community Primary Health Center. About 54.0% of participants presented with no family history of hypertension but 19.7% had a family history of type II diabetes mellitus. More women were found to be hypertensive, with the majority (93.4%) monitoring their blood pressure. The study revealed that more men were current smokers. A large number of participants were engaged in a sedentary lifestyle with about one-third of the participants reported being obese. Physical inactivity, sedentary lifestyle, and hypertension were among the lifestyle-related risk factors for chronic diseases among residents of this rural community.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.083
GPT teacher head0.328
Teacher spread0.245 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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