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Record W1978361856 · doi:10.1080/14639230600628427

Health literacy and the World Wide Web: Comparing the readability of leading incident cancers on the Internet

2006· article· en· W1978361856 on OpenAlexaff
Daniela B. Friedman, Laurie Hoffman‐Goetz, José F. Arocha

Bibliographic record

VenueMedical Informatics and the Internet in Medicine · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsReadabilityHealth literacyThe InternetReading (process)LiteracyColorectal cancerGrade levelMedicineComputer scienceWorld Wide WebCancerFamily medicineHealth careInternal medicinePsychologyMathematics education

Abstract

fetched live from OpenAlex

PRIMARY OBJECTIVE: to assess the readability level of Web-based information on leading incident cancers. RESEARCH DESIGN: websites on breast, prostate, and colorectal cancers were selected for analysis by comparing the first 100 hits across 10 popular search engines. A total of 100 websites on breast (n=33), prostate (n=34), and colorectal (n=33) cancers were included in the final analysis. METHODS: readability was assessed using SMOG, Flesch-Kincaid (F - K), and Flesch Reading Ease (FRE) measures. SMOG was hand-calculated on 10 - 30 lines of continuous text. Identical text was entered into Microsoft Word 2002 where F - K and FRE scores were determined automatically by the word processor. RESULTS: the mean readability score of the cancer websites was Grade 12.9 using SMOG and Grade 10.7 according to F - K. The mean FRE score was 45.3, a score considered 'difficult'. Colorectal cancer websites were most difficult to read compared to breast and prostate cancer websites. All measures indicated that prostate cancer websites were written at the lowest readability. Significantly higher reading levels were required for concluding paragraphs of Web articles compared to introduction paragraphs. CONCLUSIONS: findings suggest the need for readable cancer information on the Web. Health promoters, health informaticians, medical journalists, and web page editors must collaborate to ensure the use of plain language to match the literacy skills of consumers.

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)0.024
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.426
Teacher spread0.384 · 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 designSimulation or modeling
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

Citations135
Published2006
Admission routes1
Has abstractyes

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