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Low Health Literacy (LHL) Facts (Infographic)

2023· article· en· W6977448491 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsFunctional illiteracyHealth carePublic healthPovertySocioeconomic statusPopulationHealth literacyDeveloping countryGuideline

Abstract

fetched live from OpenAlex

\n A. LHL is associated with people who cherish superstitions and stigma within their preset narrow mind, which prevents them from gathering relevant health information from their surroundings. \n B. LHL has a significant impact on patients' treatment guideline compliance, or, more directly, medication adherence, which leads to poorer health outcomes, higher healthcare costs, increased hospitalizations, and even higher mortality rates. \n C. Only 12% of Americans have adequate health literacy, and improving health literacy could prevent nearly 1 million hospital visits and save more than $25 billion per year, according to the US Centers for Disease Control and Prevention (CDC). \n D. The global economic cost of illiteracy is estimated to be $1.19 trillion, but LHL alone costs the US economy $238 billion per year. \n E. Both are found in both developed and developing countries around the world, and socioeconomic factors are not the only cause of LHL. \n F. Surprisingly, nearly 40% of US and UK adults have LHL, compared to around 50% of Europeans, 60% of adults in Canada, Australia, and the UAE, and nearly 70% of Chinese. \n G. In China, health literacy increased from 6.48% of the population in 2008 to 23.15% in 2020. However, only 1 in 5 military health providers of the Chinese People's liberation Army had adequate health literacy, found in a recent survey published in BMC Public Health. \n H. Evidence suggests that LHL has significant economic consequences at the individual, employer, and healthcare system levels. \n I. The authors of the Hamburg Diabetes Prevention Survey, a population-based cross-sectional study in Germany, concluded that LHL is a significant risk factor for the metabolic syndrome's three conditions: obesity, diabetes, and hypertension. \n J. Age, place of residence, education, and family status all have an impact on health literacy. \n K. More than half of Dutch health providers use health literacy-specific materials only infrequently. \n L. Mistrust and LHL perceptions were linked to high levels of vaccine hesitancy, providing evidential support for portraying these factors as perceived barriers to COVID-19 vaccine uptake. \n M. LHL is not uncommon among patients with a high level of education or with well-off patients. Moreover, patients with LHL, but with high education, had a higher probability of emergency department re-visits. \n N. According to patient-centered interventions, improving health literacy can reduce the risk of polypharmacy, medication non-adherence, and healthcare costs. \n O. According to the 1996-2017 Medical Expenditure Panel Survey, LHL was more prevalent in glaucoma patients, and patients with LHL were prescribed more medications and had higher medication costs. \n P. Nearly 35% of diabetic patients worldwide have limited health-related education [19]. \n Q. LHL is linked to gestational diabetes, maternal stress and depression, low birth weight, stillbirth, and congenital malformations during pregnancy and birth, all of which have negative consequences for the woman and her child. \n R. Empirical research based on a conceptual model estimated that low health literacy costs between 7 and 17% of total healthcare expenditures. \n S. The prevalence of LHL in the emergency department (ED) varies greatly, with estimates as high as 88% depending on the patient mix and screening instruments used. \n T. In both low and high-income countries, low parental health literacy was linked to poorer child health outcomes. \n U. Patients who are older, have less education, a lower income, and have chronic conditions are more vulnerable. \n V. LHL was discovered in more than 70% of formal paid caregivers of non-self-supporting older adults in Tuscany, Italy, and in more than 50% of caregivers of heart failure patients in the United States. \n W. People with low health literacy may have 1.5-3 times the number of serious health outcomes, such as higher mortality, hospitalization rates, and disease management ability, as those with adequate health literacy. \n X. In cardiac patients, it has been linked to increased mortality, hospital readmission, and lower quality of life. \n Y. LHL represents nearly 50% of Germans. In Germany, every fourth to fifth person is not immunized against COVID-19. \n Z. According to a Waystar (Health Care Billing Software) report from 2019, nearly 40% of healthcare consumers were unaware that the cost of their healthcare varied across facilities. \n \n

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.476
Threshold uncertainty score0.747

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.4760.128

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 head0.129
GPT teacher head0.489
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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