Readability of online COVID-19 health information: a comparison between four English speaking countries
Bibliographic record
Abstract
BACKGROUND: The internet is now the first line source of health information for many people worldwide. In the current Coronavirus Disease 2019 (COVID-19) global pandemic, health information is being produced, revised, updated and disseminated at an increasingly rapid rate. The general public are faced with a plethora of misinformation regarding COVID-19 and the readability of online information has an impact on their understanding of the disease. The accessibility of online healthcare information relating to COVID-19 is unknown. We sought to evaluate the readability of online information relating to COVID-19 in four English speaking regions: Ireland, the United Kingdom, Canada and the United States, and compare readability of website source provenance and regional origin. METHODS: The Google® search engine was used to collate the first 20 webpage URLs for three individual searches for 'COVID', 'COVID-19', and 'coronavirus' from Ireland, the United Kingdom, Canada and the United States. The Gunning Fog Index (GFI), Flesch-Kincaid Grade (FKG) Score, Flesch Reading Ease Score (FRES), Simple Measure of Gobbledygook (SMOG) score were calculated to assess the readability. RESULTS: There were poor levels of readability webpages reviewed, with only 17.2% of webpages at a universally readable level. There was a significant difference in readability between the different webpages based on their information source (p < 0.01). Public Health organisations and Government organisations provided the most readable COVID-19 material, while digital media sources were significantly less readable. There were no significant differences in readability between regions. CONCLUSION: Much of the general public have relied on online information during the pandemic. Information on COVID-19 should be made more readable, and those writing webpages and information tools should ensure universal accessibility is considered in their production. Governments and healthcare practitioners should have an awareness of the online sources of information available, and ensure that readability of our own productions is at a universally readable level which will increase understanding and adherence to health guidelines.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".