Online information on chronic pain in 3 countries: an assessment of readability, credibility, and accuracy
Bibliographic record
Abstract
Objectives: To assess the readability, credibility, and accuracy of online information on chronic pain in Australia, Mexico, and Nepal. Methods: We assessed Google-based websites and government health websites about chronic pain for readability (using the Flesch Kincaid Readability Ease tool), credibility (using the Journal of American Medical Association [JAMA] benchmark criteria and Health on the Net Code [HONcode]), and accuracy (using 3 core concepts of pain science education: (1) pain does not mean my body is damaged; (2) thoughts, emotions, and experiences affect pain; and (3) I can retrain my overactive pain system) Results: We assessed 71 Google-based websites and 15 government websites. There were no significant between-country differences in chronic pain information retrieved through Google for readability, credibility, or accuracy. Based on readability scores, the websites were "fairly difficult to read," suitable for ages 15 to 17 years or grades 10 to 12 years. For credibility, less than 30% of all websites met the full JAMA criteria, and more than 60% were not HONcode certified. For accuracy, all 3 core concepts were present in less than 30% of websites. Moreover, we found that the Australian government websites have low readability but are credible, and the majority provided all 3 core concepts in pain science education. A single Mexican government website had low readability without any core concepts but was credible. Conclusion: The readability, credibility, and accuracy of online information on chronic pain should be improved internationally to support facilitating better management of chronic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".