This research aims to determine the usability of the central library websites of medical sciences universities in Iran. This was a descriptive, cross-sectional study which employed a heuristic evaluation method to evaluate the usability of the websites. Three trained evaluators independently examined the 12 library websites from 11 July to 10 September 2013 using Nielsen’s usability heuristics. A list of usability problems was obtained and the severity of identified problems was determined. We identified 668 usability problems. The highest number of problems concerned ‘aesthetic and minimalist design’ (27.1%) and the lowest number concerned the ‘help and documentation’ heuristic (1%). Since the importance of libraries’ websites cannot be understated, the websites should consider the identified problems and attempt to address them.

Trends in Global Assisted Reproductive Technologies Research: a Scientometrics study

This paper aims to determine novice and experienced users’ search performance and satisfaction with the interfaces of the Web of Science and Scopus. This is a descriptive-analytical study. Snowball sampling was used. Given queries were searched by 15 experienced and 15 novice users and their search performance was recorded using Camtasia. Their satisfaction was determined by the QUIS questionnaire. The experienced users were more satisfied with both databases than the novices but the difference was not significant. The results imply that even a limited amount of search experience provides search performance benefits in using databases to locate information. If a user interface is designed in a proper manner and considering users’ needs, users get better results.

Background: Despite recent progress in misinformation detection methods, further investigation is required to develop more robust fact-checking models with particular consideration for the unique challenges of health information sharing. This study aimed to identify the most effective approach for detecting and classifying reliable information versus misinformation health content shared on Twitter/X related to COVID-19. Methods: We have used 7 different machine learning/deep learning models. Tweets were collected, processed, labeled, and analyzed using relevant keywords and hashtags, then classified into two distinct datasets: “Trustworthy information” versus “Misinformation”, through a labeling process. The cosine similarity metric was employed to address oversampling the minority of the Trustworthy information class, ensuring a more balanced representation of both classes for training and testing purposes. Finally, the performance of the various fact-checking models was analyzed and compared using accuracy, precision, recall, and F1-score ROC curve, and AUC. Results: For measures of accuracy, precision, F1 score, and recall, the average values of TextConvoNet were found to be 90.28, 90.28, 90.29, and 0.9030, respectively. ROC AUC was 0.901.“Trustworthy information” class achieved an accuracy of 85%, precision of 93%, recall of 86%, and F1 score of 89%. These values were higher than other models. Moreover, its performance in the misinformation category was even more impressive, with an accuracy of 94%, precision of 88%, recall of 94%, and F1 score of 91%. Conclusion: This study showed that TextConvoNet was the most effective in detecting and classifying trustworthy information V.S misinformation related to health issues that have been shared on Twitter/X.

Background and Aims: The advent of social networking sites has facilitated the dissemination of scientific research. This article aims to investigate the presence of Iranian highly cited clinicians in social networking sites. Materials and Methods: This is a scientometrics study. Essential Science Indicator (ESI) was searched for Iranian highly cited papers in clinical medicine during November–December 2015. Then, the authors of the papers were checked and a list of authors was obtained. In the second phase, the authors' names were searched in the selected social networking sites (ResearchGate [RG], Academia, Mendeley, LinkedIn). The total citations and h-index in Scopus were also gathered. Results: Fifty-five highly cited papers were retrieved. A total of 107 authors participated in writing these papers. RG was the most popular (64.5%) and LinkedIn and Academia were in 2^nd and 3^rd places. None of the authors of highly cited papers were subscribed to Mendeley. A positive direct relationship was observed between visibility at social networking sites with citation and h-index rate. A significant relationship was observed between the RG score, citations, reads indicators in RG, and citation numbers and there was a significant relationship between the number of document indicator in Academia and the citation numbers. Conclusion: It seems putting the papers in social networking sites can influence the citation rate. We recommend all scientists to be present at social networking sites to have better chance of visibility and also citation.