Readability and cultural sensitivity of web-based patient decision aids for cancer screening and treatment: A systematic review
Bibliographic record
Abstract
Decision aids (DA) can inform cancer screening. We conducted a systematic review of web-based, cancer DA to evaluate their appropriateness for use with low literacy and diverse culture groups. Eighty-one Internet DA were found searching five databases (Pubmed-Medline; Web of Science/SSCI; Cancerlit; CINAHL; and Google) and the Cochrane decision aid inventory. Twenty-three met key inclusion criteria of (1) informing cancer screening or treatment decisions, (2) being patient or consumer oriented, and (3) conforming to the Cochrane definition of DA. DA were evaluated using the International Patient Decision Aid Standards checklist, the Cultural Sensitivity Assessment Tool (CSAT), the Cultural Sensitivity Assessment Checklist (CSAC), and the SMOG readability formula. DA had a high readability with 74% (n = 17) written at the grade 10 - 13, 22% (n = 5) at the grade 9, and 4% (n = 1) at the grade 8 level. Visual aids were used in 35% (n = 8) to present probability information. Written information was complemented with video or audio components in 35% (n = 8). Most (91%, n = 21) were developed for generic audiences, while 9% (n = 2) specified a cultural group. Although DA enabled a step-by-step movement through the website, none allowed key word searches and only 65% permitted document printing. Most DA included difficult texts and were not focused for specific cultural groups.
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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 | 0.023 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".