Content and Quality of Information Websites About Congenital Heart Defects Following a Prenatal Diagnosis
Notice bibliographique
Résumé
BACKGROUND: Pregnant women and their partners use the Internet to search for information following a prenatal diagnosis of congenital heart defect. OBJECTIVE: Our aim was to explore central subjects of content and to assess the accessibility, reliability, usability, and quality of written information on publicly available information websites about congenital heart defects following a prenatal diagnosis. METHODS: Following searches on Bing and Google, we included websites containing patient information in English. Hits ranged from 340,000-67,500,000 and the first 50 hits from each search were screened for inclusion (N=600). Of these hits, 39.3% (236/600) were irrelevant. A total of 67 websites were included, of which 37% (25/67) were affiliated with independent information websites, 25% (17/67) with charity/private organizations, 25% (17/67) with hospitals/clinics, and 13% (8/67) had other affiliations. The majority of the websites (76%, 51/67) could not be attributed to an author. A manifest content analysis was performed to explore central subjects of content. The DISCERN instrument was used to assess the quality of information, and the LIDA tool was used to assess accessibility, usability, and reliability of the included websites. RESULTS: The content on the majority of the websites included care and treatment of children with congenital heart defects (88%, 59/67), causes of congenital heart defects (88%, 59/67), symptoms of congenital heart defects (85%, 57/67), prevalence of congenital heart defects (81%, 54/67), potential complications of congenital heart defects (75%, 50/67), prenatal diagnostics/screening methods (72%, 48/67), and specific congenital heart defects (72%, 48/67), whereas less than 10% included information about termination of pregnancy (6%, 4/67), care during pregnancy (5%, 3/67), and information specifically directed to partners (1%, 1/67). The mean of the total DISCERN score was 27.9 (SD 9.7, range 16-53). According to the instrument, a majority of the websites were categorized as very poor regarding information about effects of no treatment (88%, 59/67), support for shared decision making (85%, 57/67), achievement of its aims (84%, 56/67), explicit aims (82%, 55/67), risks of each treatment (82%, 55/67), how treatment choices affect overall quality of life (76%, 51/67), and areas of uncertainty (76%, 51/67). The mean of the total LIDA score was 92.3 (SD 13.1, range 61-127). According to the tool, a majority of the websites were categorized as good with regard to registration (97%, 65/67) and browser test (75%, 50/67), whereas a majority were categorized as poor with regard to currency (87%, 58/67), content production (84%, 56/67), and engagability (75%, 50/67). CONCLUSIONS: Difficulties in finding relevant information sources using Web search engines and quality deficits on websites are an incentive for health professionals to take an active part in providing adequate and reliable information online about congenital heart defects.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,052 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,008 | 0,008 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».