Evaluation of the scope, quality, and health literacy demand of Internet-based anal cancer information
Bibliographic record
Abstract
OBJECTIVES: As there is a dearth of information about anal cancer available at cancer centers, patients often use the Internet to search for information. This is problematic, however, because the quality of information on the Internet is variable, and the health literacy demanded is higher than the average patrons' capacity. The purposes of this study were to (1) determine the most common websites with anal cancer consumer health information, (2) identify the supportive care needs that each website addresses, and (3) evaluate the websites' quality and health literacy demand. METHODS: Medical Subject Headings (MeSH) entry terms for "Anus Neoplasms" were used in Google Canada to identify websites. Seven domains of supportive care needs were defined using Fitch's Supportive Care Framework for Cancer Care. Website quality was evaluated using the DISCERN tool. Health literacy demand was assessed using readability calculators, where best practice dictates a grade 6 or lower, and the Patient Education Material Assessment Tool (PEMAT) that computes a percentage score in 2 domains, understandability and actionability, with 80% being an acceptable score. RESULTS: Eighteen unique websites were evaluated. One website met health literacy best practices and had a "good" quality rating. Most websites addressed only 1 supportive care domain (61%), were of "fair" quality (67%), had readability scores higher than grade 6 (89%), and had PEMAT scores ranging from 41%-92% for understandability and 0-70% for actionability. CONCLUSION: The information gaps on anal cancer websites warrant a need for more health literate anal cancer health information on the Internet.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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".