Health literacy and the World Wide Web: Comparing the readability of leading incident cancers on the Internet
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".