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Record W2008507630 · doi:10.3390/ijerph10031125

Health Literacy in Rural Areas of China: Hypertension Knowledge Survey

2013· article· en· W2008507630 on OpenAlexafffund
Xia Li, Ning Ning, Yanhua Hao, Hong Sun, Lijun Gao, Mingli Jiao, Qunhong Wu, Hude Quan

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Calgary
FundersAlberta InnovatesChina Medical Board
KeywordsFunctional illiteracyMedicineMarital statusRural areaChinaHealth literacyLiteracyEducational attainmentPopulationBlood pressureHealth educationEnvironmental healthGerontologyDemographyFamily medicinePsychologyHealth careNursingGeographyPublic healthEconomic growthPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

We conducted this study to determine levels and correlates of hypertension knowledge among rural Chinese adults, and to assess the association between knowledge levels and salty food consumption among hypertensive and non-hypertensive populations. This face-to-face cross sectional survey included 665 hypertensive and 854 non-hypertensive respondents in the rural areas of Heilongjiang province, China. Hypertension knowledge was assessed through a 10-item test; respondents received 10 points for each correct answer. Among respondents, the average hypertension knowledge score was 26 out of a maximum of 100 points for hypertensive and 20 for non-hypertensive respondents. Hypertension knowledge was associated with marital status, education, health status, periodically reading books, newspapers or other materials, history of blood pressure measurement, and attending hypertension educational sessions. Hypertension knowledge is extremely low in rural areas of China. Hypertension education programs should focus on marginal populations, such as individuals who are not married or illiterate to enhance their knowledge levels. Focusing on educational and literacy levels in conjunction with health education is important given illiteracy is still a prominent issue for the Chinese rural population.

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)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.127
GPT teacher head0.498
Teacher spread0.371 · 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.

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

Citations57
Published2013
Admission routes2
Has abstractyes

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