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Record W4236187143 · doi:10.2196/preprints.15425

Assessing the Quality and Readability of Health Information Webpages on Chronic Conditions (Preprint)

2019· preprint· en· W4236187143 on OpenAlexaboutno aff
Alexandra Hordern Kellington, Julie N Babione, Jaime Kaufman, Doreen M. Rabi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsReadabilityMedicineWeb pageChecklistThe InternetPatient educationQuality (philosophy)World Wide WebFamily medicineComputer sciencePsychology

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.003
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.159
GPT teacher head0.557
Teacher spread0.398 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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