Assessment of Canadian patient education material for oncology pharmaceutics
Bibliographic record
Abstract
INTRODUCTION: Health literacy is an individual's ability to access, understand, and utilize information in order to create an informed decision regarding their health. Readability plays an integral role in health literacy as complex health information may be inaccessible to those with low health literacy. The aim of this study is to determine the readability of Canadian patient education material (PEM) for oncology related pharmaceutics. METHODS: Eighty PEMs from Cancer Care Ontario (CCO) and BC Cancer (BCC) were evaluated for their reading level using a Ford, Caylor, Sticht (FORCAST) analysis. Twenty therapies were then randomly selected and converted to plain text to be analyzed further using the Flesch-Kincaid Grade Level (FKGL), the Simple Measure of Gobbledygook (SMOG) Index, the Coleman-Liau Index (CLI), and the Gunning Fog Index (GFI). RESULTS: Both PEMs from CCO and BCC were above the recommended reading level with PEMs from CCO, on average, requiring a higher reading level. Within the text, the section which describes side effects was found to be the most complex section of the representative PEMs from BCC. PEMs from BCC which described antibody-based therapies were, on average, more difficult to read than small molecule-based therapies regardless from which section the PEM was being analyzed. These observations were not seen in CCO PEMs. CONCLUSIONS: Overall, online PEMs from major Canadian cancers associations were written above the recommended reading level. Consideration should be given to revision of these materials, with emphasis on the therapies' side effects, to allow for greater comprehension amongst a wider target audience.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".