Readability of English Wikipedia's health information over time
Bibliographic record
Abstract
Objective: To assess and compare the readability of the twenty-five most accessed English medical articles on Wikipedia 0, 1, 5 and 10 years ago.Design: The twenty-five most accessed Wikipedia articles on diseases in August 2018 were identified for this study.The content of the lead paragraphs was formatted to remove any hyperlinks, decimals, colons, semicolons and periods used in abbreviations.An online tool was then used to assign a score to the readability of each text sample using the following formulae: Gunning FOG (Frequency of Gobbledygook) index, Flesch-Kincaid Grade Level (F-K), Simple Measure of Gobbledygook (SMOG) and Flesch Reading Ease (FRE).A single reading grade (RG) was calculated for each passage by averaging scores from the FOG, SMOG and F-K tests to facilitate interpretation.These steps were repeated for the lead paragraph of the same medical articles as visible 1, 5 and 10 years ago on Wikipedia. Main Outcome Measures: Readability grade (RG) and reading ease (FRE score)Results: The average (mean) RG of the twenty-five most accessed Wikipedia articles on diseases in 2018 was 12.73 (95% CI = 12.07-13.38),and the average FRE score was 39.91 (95% CI = 36.09-43.74),a score considered "difficult".The number of articles that were easier to read (lower RG and higher FRE) in 2018 was significantly higher when compared to 2013 and 2008 (p<0.0001),but not significantly different when compared to 2017.When paired by titles and compared over time, a statistically significant difference in readability (RG and FRE) was seen in 2018 when compared to earlier years: 2017 (Friedman Chi-squared=13.70,p=0.0002), 2013 (Friedman Chi-squared=46.08,p<0.0001) and 2008 (Friedman Chi-squared=33.03,p=0.0001).None of the pages were written at the 7th or 8th grade level as recommended by the U.S. National Institutes of Health (NIH). Conclusions:The average readability of English Wikipedia's medical pages has improved in 2018 when compared to previous years.Most of the health information, however, remains written at a level above the reading ability of average adults.
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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 | 0.003 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".