Assessing the Quality and Readability of Health Information Webpages on Chronic Conditions (Preprint)
Bibliographic record
Abstract
BACKGROUND Chronic conditions are highly prevalent in Canada, and individuals living with one or more chronic conditions often have complex care needs. Patient empowerment and self-management are central components of modern chronic disease management and one of the ways patients with chronic conditions self-manage their health is through the use of online resources and tools. There is, however, no regulation of information on the internet and patients may use potentially harmful or misleading information to inform their clinical decisions. OBJECTIVE The current study aimed to assess the quality and readability of easily accessible online health information resources. METHODS This study utilized Google and a number of other health information websites to search for information on seven common chronic conditions (asthma, chronic kidney disease, cardiovascular disease, chronic obstructive pulmonary disease, diabetes, hypertension, and mental health). Eligible webpages were then checked for quality using a 13-point checklist and for readability using the Dale-Chall readability formula. RESULTS : A total of 123 webpages were found that contained health information intended for the public. The quality scores ranged from 6/13 to 13/13. A majority (79.7%, N=98) of these webpages were high quality. The remaining webpages were medium quality. The webpage with the highest readability was written at a ninth to tenth grade level. The most common readability score was college graduate (41.5%, N=51). Taken together, the number of webpages that required post-secondary education to understand them was 92 (74.8%). CONCLUSIONS Health information identified through simple and practical searching methods was overall of high quality but had low readability. This study suggests that freely available information may not be universally accessible, particularly to those with lower educational attainment that may also be at higher risk of poor health outcomes.
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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 | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 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) | 0.007 | 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".