MétaCan
Menu
Back to cohort
Record W3159249937 · doi:10.1097/phh.0000000000001352

Health Literacy Among Adults With Multiple Chronic Health Conditions

2021· article· en· W3159249937 on OpenAlexaff
Ann P. Rafferty, Huabin Luo, Nancy L. Winterbauer, Ronny A. Bell, Nancy Little, Satomi Imai

Bibliographic record

VenueJournal of Public Health Management and Practice · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsHealth literacyOdds ratioConfidence intervalLogistic regressionOddsBehavioral Risk Factor Surveillance SystemLiteracyMedicineEnvironmental healthPopulationDemographyGerontologyPsychologyHealth careInternal medicine

Abstract

fetched live from OpenAlex

Low health literacy (HL) is associated with poorer health outcomes. We examined HL among adults with multiple chronic conditions (CCs), using 2016 Behavioral Risk Factor Surveillance System data. Health literacy was measured by 3 subjective questions about difficulty with the following tasks: (1) obtaining health information or advice; (2) understanding spoken health information; and (3) understanding written health information. We estimated the prevalence of low HL (difficulty with ≥1 HL tasks) and used multiple logistic regression analysis to examine associations between HL and number of CCs. The prevalence of low HL was 13.8% overall and increased with the number of CCs from 10.6% among those with no CC to 24.7% among those with 3 or more CCs, with the latter having more than twice the adjusted odds of low HL compared with the former (adjusted odds ratio = 2.65; 95% confidence interval, 2.36-2.97). Efforts to improve HL in this population are needed.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.071
GPT teacher head0.470
Teacher spread0.398 · 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.

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

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Public Health Management and PracticeSame topicHealth Literacy and Information AccessibilityFrench-language works237,207