Quality Assessment of Web-Based Information Related to Diet During Pregnancy in Pregnant Women: Cross-Sectional Descriptive Study
Notice bibliographique
Résumé
BACKGROUND: The widespread availability of health information online, coupled with the ease of access to the internet, has led pregnant women to rely heavily on online sources for pregnancy-related guidance. The internet-based information regarding nutrition enabled positive dietary changes for pregnant women. Although there are some important sources for pregnant women to collect their health information, some information increases maternal anxiety and difficulties based on a lack of information. Moreover, some women become confused due to conflicts on the same topics from different websites. However, concerns about the reliability and impact of this information have surfaced, contributing to heightened anxiety among expectant mothers. The importance of the quality of web-based information is increasingly recognized; however, no studies have evaluated the quality of nutrition-related information for pregnant women. OBJECTIVE: This study aims to bridge this research gap by assessing the quality of online health information concerning prenatal nutrition tailored to pregnant women. METHODS: This cross-sectional descriptive study was conducted through a Google keyword search on February 14, 2023. We used search terms, such as "pregnancy," "pregnant women," "diet," and "nutrition" and conducted an exhaustive search on Google. Using the Quality Evaluation Scoring Tool (QUEST), we meticulously evaluated the quality of the retrieved information. RESULTS: The top 20 Google-searched sites were evaluated using the QUEST tool. The average score was 11.7 points, ranging from 6 to 15, with most sites scoring between 11 and 15. Half of the websites lacked clear authorship and most gave weak or no attribution to specific scientific sources. While conflict of interest scored highest overall, with 60% showing no bias, some sites promoted products or specific interventions. Currency was inconsistent-only half were updated within 5 years. Complementarity received the lowest scores, with 70% lacking support for patient-physician relationships. The tone was generally positive, with 95% supporting their claims, though only one site used a balanced, well-reasoned tone. Discrepancies in cited guidelines on nutritional intake and inappropriate expressions about alcohol, weight management, and miscarriage raised concerns about the information's accuracy and appropriateness. CONCLUSIONS: Although many websites use cautious language to mitigate commercial influence, deficiencies persist in crucial areas for empowering informed decision-making among pregnant women. From our assessment of the results, it was found that incorrect evidence information is provided at the top of search results, which is easily accessible to users. The inadequacies in attributing authorship, clarifying conflicts of interest, and ensuring the currency of information pose substantial challenges to the reliability and usefulness of online health resources in prenatal nutrition. Since internet-based information is the most accessible, reliable evidence should be provided to protect everyone from misinformation, including shallow health literacy demographics, and from potential physical and psychological harm.
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 | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».