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Enregistrement W6977448491 · doi:10.6084/m9.figshare.22012628.v1

Low Health Literacy (LHL) Facts (Infographic)

2023· article· en· W6977448491 sur OpenAlexaboutno aff

Notice bibliographique

RevueFigshare · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealth Literacy and Information Accessibility
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFunctional illiteracyHealth carePublic healthPovertySocioeconomic statusPopulationHealth literacyDeveloping countryGuideline

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,476
Score d'incertitude au seuil0,747

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,000
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,4760,128

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,129
Tête enseignante GPT0,489
Écart entre enseignants0,360 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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