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Record W4285045674 · doi:10.1016/j.cjco.2022.07.004

Evaluation of Online Written Medication Educational Resources for People Living With Heart Failure

2022· article· en· W4285045674 on OpenAlexafffund
Simroop Ladhar, Sheri L. Koshman, Felicia Yang, Ricky D. Turgeon

Bibliographic record

VenueCJC Open · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsHeart failureMedicinePsychologyGerontologyCardiology

Abstract

fetched live from OpenAlex

Background: Patient educational resources on heart failure (HF) medications may improve patient understanding, which is critical for informed decision-making and patient self-efficacy. The purpose of our study was to evaluate the quality and readability of written medication educational resources available online. Methods: Two investigators searched Google, Yahoo, and Bing for written patient educational resources that addressed at least one HF medication. We assessed educational quality using the Ensuring Quality Information for Patients (EQIP) tool (range 0 [worst] to 100 [best]), and we evaluated readability using the Flesch-Kincaid Grade Level. Results: From 693 identified webpages, 39 HF medication educational resources met study eligibility. Among included resources, the median Ensuring Quality Information for Patients score was 61% (interquartile range 54%-68%), with 2 (5%) rated as high quality (score ≥ 75%). The median Flesch-Kincaid Grade Level was 8 (interquartile range 8-12), with 4 (10%) resources meeting the recommended 6th-grade reading level. Conclusions: Most HF medication educational resources available on the Internet are of acceptable educational quality, but could readily be improved. Most resources were beyond the recommended reading grade level for educational resources, limiting their utility for patients with a low literacy level.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.078
GPT teacher head0.488
Teacher spread0.410 · 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 teacher head, not a consensus.

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".

Quick stats

Citations9
Published2022
Admission routes2
Has abstractyes

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