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Record W2756371906 · doi:10.2196/ijmr.7844

Quality of Patient Information Websites About Congenital Heart Defects: Mixed-Methods Study of Perspectives Among Individuals With Experience of a Prenatal Diagnosis

2017· article· en· W2756371906 on OpenAlexvenueno aff
Tommy Carlsson, Ulla Melander Marttala, Barbro Wadensten, Gunnar Bergman, Ove Axelssön, Elisabet Mattsson

Bibliographic record

VenueInteractive Journal of Medical Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHeart defectScarcityQuality (philosophy)Prenatal diagnosisInformation qualityPsychologyFetusHeart diseaseInformation systemPregnancyPathologyEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: When a heart defect is prenatally diagnosed in the fetus, expectant parents experience a great need for information about various topics. After the diagnosis, the Web is used for supplemental information, and the scarcity of research calls attention to the need to explore patient information websites from the perspectives of the intended consumers. OBJECTIVE: The overarching aim of this study was to explore the quality of Swedish patient information websites about congenital heart defects, from the perspectives of individuals with experience of a prenatal diagnosis of congenital heart defect in the fetus. METHODS: This was a mixed-methods study of websites identified through systematic searches in the two most used Web-based search engines. Of the total 80 screened hits, 10 hits led to patient information websites about congenital heart defects. A quality assessment tool inspired by a previous study was used to evaluate each website's appearance, details, relevance, suitability, information about treatment choices, and overall quality. Answers were given on a 5-point Likert scale, ranging from 1, representing the lowest score, to 5, representing the highest score. Each website was assessed individually by persons with experience of continued (n=4) and terminated (n=5) pregnancy following a prenatal diagnosis. Assessments were analyzed with Kendall's coefficient of concordance W, Mann-Whitney U test, Friedman's test, and a Wilcoxon-Nemenyi-McDonald-Thompson test. In addition, each assessor submitted written responses to open-ended questions in the quality assessment tool, and two joint focus group discussions were conducted with each group of assessors. The qualitative data were analyzed with inductive manifest content analysis. RESULTS: Assessments represented a low score (median=2.0) for treatment choices and moderate scores (median=3.0) for appearance, details, relevance, suitability, and overall quality. No website had a median of the highest achievable score for any of the questions in the quality assessment tool. Medians of the lowest achievable score were found in questions about treatment choices (n=4 websites), details (n=2 websites), suitability (n=1 website), and overall quality (n=1 website). Websites had significantly different scores for appearance (P=.01), details (P<.001), relevance (P<.001), suitability (P<.001), treatment choices (P=.04), and overall quality (P<.001). The content analysis of the qualitative data generated six categories: (1) advertisements, (2) comprehensiveness, (3) design, (4) illustrations and pictures, (5) language, and (6) trustworthiness. Various issues with the included websites were highlighted, including the use of inappropriate advertisements, biased information, poor illustrations, complex language, and poor trustworthiness. CONCLUSIONS: From the perspectives of the intended consumers, patient information websites about congenital heart defects are, to a large extent, inadequate tools for supplemental information following a prenatal diagnosis. Health professionals should initiate discussions with patients about their intentions to use the Web, inform them about the varied quality in the Web-based landscape, and offer recommendations for appropriate Web-based sources.

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)0.022
metaresearch head score (Gemma)0.072
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient 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 score0.093
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.072
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.004
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.107
GPT teacher head0.604
Teacher spread0.497 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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