Lost in translation: Assessing the readability of online information on community pharmacy services
Bibliographic record
Abstract
Background: The World Health Organization’s right to health underscores the need for accessible, acceptable, and quality health services. Given that most Canadians use the Internet for health information, the readability of online pharmacy services information is crucial for accessibility. Methods: This study assessed the readability of online information about pharmacy services from Canadian provincial pharmacy regulatory authorities (PRAs) and community pharmacy banners. Public-facing website content was evaluated using various readability tests. Scores were compared to recommended reading grade levels by health organizations, and differences between PRA and community pharmacy banner websites were analyzed. Results: Website content from 9 PRAs and 10 community pharmacy banners was analyzed in June 2024. Average readability scores exceeded the recommended eighth-grade level, with summary scores ranging from 8.45 to 15.28. International English Language Testing System scores for all websites also surpassed reading benchmarks necessary for Canadian immigration. Mann–Whitney U tests indicated statistically significant differences between PRA and community pharmacy banner websites, with the latter being more readable. Discussion: The results suggest that both PRAs and community pharmacy banners provide information at an advanced reading level, hindering accessibility. This aligns with other research indicating that online health information is often too complex for the general public. Improving readability, particularly for new Canadians, is essential for better accessibility. Conclusion: Public health information on PRA and community pharmacy banner websites generally exceeds the recommended readability level, limiting accessibility. Implementing readability assessments and plain-language standards can enhance the accessibility and engagement of online health information.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".