Evaluating the Health Literacy Burden of Canada’s Public Advisories: A Comparative Effectiveness Study on Clarity and Readability
Bibliographic record
Abstract
BACKGROUND: Significant knowledge gaps exist related to evaluating health product risk communication effectiveness in a regulatory setting. To this end, Health Canada is assessing methods to evaluate the effectiveness of their health product risk communications in an attempt to identify best practices. OBJECTIVE: We examined the health literacy burden of Public Advisories (PAs) before and after implementation of a new template. We also compared two methods for their usefulness and applicability in a regulatory setting. METHODS: Suitability assessment of materials (SAM) and readability tests were run by three independent evaluators on 46 PAs (14 "Pre-format change" and 32 "Post-format change"). These tests provided adequacy scores for various health literacy elements and corresponding scholastic grades. RESULTS: PAs using the new template scored better, with an average increase of 18 percentage points (p < 0.001), on the SAM test. All of the 46 PAs evaluated were rated as "requiring a college/university education comprehension level" using readability tests. Results among readability tests were comparable. CONCLUSION: Improvements made to Health Canada's PA template had a measurable, positive effect on reducing the health literacy burden, based on the SAM results. A greater focus on the use of plain language would likely add to this effect. The SAM test emerged as a robust, reliable, and informative health literacy tool to assess risk messages and identify further improvement efforts. Regulators, industry, and public sector organizations involved in communicating health product risk information should consider the use of this test as a best practice to evaluate health literacy burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".