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Record W1563209007 · doi:10.24095/hpcdp.29.1.01

Health Literacy and Numeracy: Key Factors in Cancer Risk Comprehension

2008· article· en· W1563209007 on OpenAlexafffundvenueabout
Lorie Donelle, José F. Arocha, Laurie Hoffman‐Goetz

Bibliographic record

VenueChronic diseases in Canada · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of WaterlooWestern University
FundersCanadian Institutes of Health Research
KeywordsNumeracyHealth literacyContext (archaeology)LiteracyComprehensionMedicineAnxietyHealth carePsychologyDevelopmental psychologyComputer sciencePsychiatryPedagogy

Abstract

fetched live from OpenAlex

In this age of chronic disease and shared decision making, individuals are encouraged to contribute to decisions about health care. Health literacy, including numeracy, is requisite to meaningful participation and has been accepted as a determinant of health. The purpose of this study was to describe the influence of literacy, consisting of prose and numeracy skill, math anxiety, attained education and context of information on participant ability to comprehend Internet-based colorectal cancer prevention information. Prose, numeracy, and math-anxiety data, as well as demographic details, were collected for 140 Canadian adults, aged 50 + years. Participants had adequate prose literacy (STOFHLA) scores, high STOFHLA numeracy scores, moderate levels of health-context numeracy, poorer general-context numeracy and moderate math anxiety. There was better comprehension by participants of common (9.14/11) compared with uncommon (7.64/11) colorectal cancer information (p < 0.01). Prose literacy, numeracy, math anxiety and attained education accounted for 60% of the variation in participant comprehension scores. Numeracy, ranging from basic to advanced proficiency, is required to understand online cancer risk information. Prose literacy enhances numeracy when the subject matter is less familiar. These findings highlight the importance of presenting Web-based information that accommodates diverse health literacy and numeracy levels.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.073
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.406
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.

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

Citations63
Published2008
Admission routes4
Has abstractyes

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