Quality Assessment of Web-Based Information Related to Diet During Pregnancy in Pregnant Women: Cross-Sectional Descriptive Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".