Assessing the Reliability of YouTube as a Patient Education Tool for Assisted Reproductive Technologies

Assessing the Reliability of YouTube as a Patient Education Tool for Assisted Reproductive Technologies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the Reliability of YouTube as a Patient Education Tool for Assisted Reproductive Technologies Selim Demirtaş, Mehmet Nuri Gördük This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5803338/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Dec, 2025 Read the published version in Journal of Medical and Dental Investigations → Version 2 posted You are reading this latest preprint version Show more versions Abstract Background Assisted reproductive techniques (ART) have made significant progress in infertility treatment and have found a wide range of applications with various methods, especially in vitro fertilisation. Today, digital platforms such as YouTube have become an important resource for individuals seeking information about ART. However, uncertainties about the reliability and quality of this content pose a potential risk for viewers. This study aims to assess the quality and reliability of ART-related videos on YouTube. Methods 76 videos were analyzed by applying elimination criteria from 350 videos uploaded between October 1, 2014 and October 1, 2024. These videos were obtained through YouTube Data API v3 by searching seven different terms related to in vitro fertilization and infertility. In addition to descriptive statistics on video features, the Global Quality Scale (GQS), Modified DISCERN, JAMA and UTvAC scales were applied to evaluate the quality of the videos. Additionally, sentiment analysis was conducted on the comments of the videos. Results It was determined that the majority of the analyzed videos were at medium quality level. While the JAMA scores indicate that most of the videos are of low quality, the GQS and Modified DISCERN scores suggest that most videos fall within the medium quality range. According to UTvAC, most videos are just below the high-quality limit. The majority of videos were uploaded by doctor-independent influencers (32.9%), non-governmental organizations (25.0%), and news/media (21.1%) channels. The positive comment percentage was found to be 28%, indicating that the videos were generally poorly appreciated by viewers. Conclusion This study revealed that the information quality and reliability of ART videos on YouTube are generally moderate. It is recommended that viewers should adopt a critical approach and turn to reliable sources when evaluating content on ART. The findings of the study show that there is a significant need for improvements in digital health communication and educational content production. ART Youtube GQS JAMA DISCERN UTvAC Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Assisted reproductive techniques (ART) have made significant progress in the field of infertility treatment. After the first successful in vitro fertilization (IVF) procedure and the birth of the first "test tube baby" named Louise Brown in 1978, ART methods developed rapidly and found a wide range of applications ( 1 , 2 ). Apart from IVF, techniques such as Intrauterine insemination (IUI), intracytoplasmic sperm injection (ICSI) and genetic screening are also widely used in infertility treatment ( 3 ). Technological innovations in this field have made treatment processes safer, more effective and accessible. While ART applications make individuals' and couples' hopes of having children come true, increasing the level of knowledge and understanding of treatment processes has also gained importance. Founded in 2005, YouTube has grown rapidly as the internet's largest video sharing platform and has reached billions of users today ( 4 ). YouTube has approximately 2.7 billion active users worldwide as of 2024. This means that around 52% of internet users worldwide access YouTube at least once every month, and 56.7% of social media users worldwide use YouTube ( 5 ). YouTube has become an active source of information not only for entertainment and social content, but also in important areas such as education and health ( 6 ). In particular, content in the field of health facilitates users’ access to medical information ( 6 , 7 ). There is no exact percentage of YouTube users worldwide who watch health content. However, some studies and data show that health topics are followed by a significant audience on YouTube ( 8 ). There are many videos about ART on YouTube and we could not find any direct study on this subject in the literature. Existing studies examine a specific topic ( 9 , 10 ). In this study, we tried to evaluate how useful and reliable ART videos on YouTube are for patients through four score analyses. Materials and Methods Data Collection We conducted a study of YouTube videos using the YouTube Data API v3 on October 1, 2024. The search was performed with the following keywords: 'IVF fertilization', 'infertility treatment', 'male infertility', 'female infertility', 'IVF baby', 'assisted reproductive technology', and 'IVF procedure'. The YouTube API provided direct access to video metadata, including video ID, title, author, channel description, channel keywords, channel subscribers, publication date, duration, views, total comment count, and likes. The channels were categorized into six groups based on the authors of the videos. Inclusion and Exclusion Criteria VideoSelection: For each keyword, the first 50 videos were selected, resulting in an initial pool of 350 videos. Duplicate videos were subsequently removed. Video Duration: Videos shorter than 60 seconds were excluded to eliminate brief, non-informative clips. Recency: Videos published more than 10 years prior to the search date were excluded to ensure the data reflect recent discussions and up-to-date medical information. Comment Analysis: Videos with fewer than 10 comments were excluded, as the research focuses on sentiment analysis of comments. Language: Only videos in English were included to ensure consistency and comprehensibility during analysis. Final Dataset: After applying all exclusion criteria, a total of 76 videos were included in the study (Figure 1). Comment Retrieval and Preprocessing and Sentiment Analysis Using the YouTube Data API, we retrieved the top-level comments (i.e., main comments without replies) from each of the selected videos. The focus on top-level comments was to ensure that the analysis captures the primary discussions initiated by viewers, rather than responses, which may not reflect opinions about video but answer to a comment. Comments were preprocessed to remove irrelevant elements such as emojis, URLs, special characters, and non-alphanumeric symbols. Additionally, single-character comments (e.g., "!", ".", etc.) were excluded from the analysis to enhance the relevance and clarity of the data. Sentiment analysis was performed on the cleaned comments to evaluate the tone of viewer discussions. We used the twitter-roberta-base-sentiment model, a transformer-based approach known for accurately capturing nuanced sentiments, including sarcasm and context-specific expressions. Comments were classified as positive, neutral, or negative, and the overall sentiment distribution and mean sentiment score were calculated for each video. Scales Four different scales were used to evaluate the quality of the videos: Global Quality Score (GQS), modified DISCERN, Journal of American Medical Association (JAMA) benchmark criteria and YouTube Video Assessment Criteria (UTvAC) (11-14). Each video was independently watched and assessed by the authors, and the average score of the authors was considered in the analysis. As the study utilized publicly available open-source data, ethical approval was not required. All data collection, extraction, correction, and analysis were performed using Python and R programming languages, along with their relevant libraries. Results To visualize the most frequently used keywords in the channel descriptions of the videos, a word cloud was created. As shown in Fig. 2 , the most prominent keywords include IVF , medical , news , fertility , health , and infertility , followed by terms like doctor , treatment , and biology . In this study, 76 YouTube videos were analyzed, and the characteristics outlined in Table 1 were examined. After reviewing the videos, 31.6% of them were either direct advertisements or included advertisements alongside informational content. Table 1 Descriptive Statistics of Video Metrics and Quality Scores. Variable mean sd median min max Subscribers 1636134 3557661 284000 934 20700000 Views 252454 582062 59392 2751 3155754 Likes 2389 6392 505 0 42319 Comments 228 762 49,5 10 6394 Days on Youtube 1535 887 1465 142 3455 Length(seconds) 554 594 324 63 3355 GQS 3,39 0,818 3,5 2 5 Modified DISCERN 3,41 0,912 4 1 5 JAMA 2,07 0,574 2 1 4 UTvAC 12,3 1,29 12 7 15 The distribution of video lengths shows a right-skewed pattern, as most of the videos are concentrated in the 0–15 minute range ( Fig. 3 ). The quality scores (GQS, Modified DISCERN, JAMA, and UTvAC) indicate that most videos tend to fall in the moderate quality range across all scales. GQS and Modified DISCERN scores are distributed similarly, with most videos scoring between 3 and 4 points. The JAMA scores, however, are notably low, with the majority of videos clustered at 2 points. This suggests that the videos generally meet minimal quality standards according to the JAMA criteria. For the UTvAC, the scores are concentrated around 12 points, with fewer videos scoring at the lower or upper extremes. Given that the UTvAC considers 13 points and above as indicative of high-quality videos, this suggests that most videos fall just below the threshold for high quality, reflecting a moderate evaluation of video quality based on this scale (Fig. 4 ). Channels were grouped according to their descriptions and keywords and six different categories were created. Among these categories, unaffiliated doctors (grouped as “doctor independent influencers”) had the highest representation, followed by non-governmental organizations and News/Media channels. In the analysis conducted according to channel groups, educational channels had the highest mean views, views per day, likes, and comments. However, the "doctor independent influencer" group received relatively more likes, although it received fewer views compared to other groups. The Kruskal-Wallis test was used to analyze the relationship between the variables in Table 2 and the channel groups, and only the comments were found to be statistically significant. Table 2 Descriptive summary of variables by channel group Mean ± sd variable Doctor independent influencer Non-government organization News/media University/ hospital/ medical organization Independent influencer Education p-value* n(%) 25(32.9) 19(25.0) 16(21.1) 10(13.2) 4(5.3) 2(2.6) Likes 1644.5 ± 3668.9 1069.5 ± 2122.0 3113.4 ± 7880.9 1964.0 ± 2478.7 828.8 ± 1263.3 23682.0 ± 26356.7 0.054 Views 165967.2 ± 375118.4 267322.3 ± 663609.3 185843.6 ± 445910.0 304539.9 ± 507272.4 138205.2 ± 109789.7 1693231.5 ± 2068319.2 0.065 Views per day 141 ± 320 153 ± 296 211 ± 469 306 ± 747 83.2 ± 23.1 507 ± 574 0.266 Comments 99.6 ± 125.5 62.1 ± 76.1 564.8 ± 1580.8 230.0 ± 276.6 100.5 ± 142.6 959.0 ± 995.6 0.040 Positive Comments 18.8 ± 23.9 10.4 ± 13.6 53.1 ± 114.2 45.6 ± 69.6 30.0 ± 50.1 231.0 ± 164.0 0.844 GQS 3.5 ± 0.8 3.3 ± 0.7 3.5 ± 0.8 3.5 ± 1.0 2.2 ± 0.5 3.5 ± 0.7 0.151 Modified DISCERN 3.6 ± 0.9 3.2 ± 1.0 3.6 ± 0.7 3.4 ± 1.0 2.2 ± 0.5 3.5 ± 0.7 0.096 JAMA 2.2 ± 0.7 1.9 ± 0.3 2.1 ± 0.4 2.2 ± 0.6 1.5 ± 0.6 2.0 ± 0.0 0.120 UTvAC 12.3 ± 1.5 12.1 ± 0.5 12.2 ± 1.0 12.4 ± 2.1 12.0 ± 0.8 14.0 ± 1.4 0.090 *: The Kruskal-Wallis test was applied, with a significance level set at p < 0.05. Approximately 9,000 main comments from the videos examined were subjected to sentiment analysis. According to the analysis, 28% of the comments were classified as positive. Comments with positive sentiments received higher scores, and it was understood that the comments grouped as positive by the model were actually highly positive according to the model's evaluation (Table 3 ). Table 3 Distribution of Sentiment Analysis Results type n(%) Sentiment mean(sd) positive 2556(28.0) 0.81(0.2) negative 3295(36.1) 0.70(0.2) neutral 3287(36.0) 0.68(0.1) Videos were categorized into four groups based on their length: 1–5 minutes, 5.01-10 minutes, 10.01-15 minutes , and greater than 15 minutes. The relationship between these video length groups and scale scores was analyzed using the Kruskal-Wallis test , as none of the scale scores followed a normal distribution according to the Shapiro-Wilk test. For the Global Quality Score (GQS), no statistically significant difference was observed between the groups. However, for the JAMA, UTvAC, and Modified DISCERN scales, the differences between groups were statistically significantTo identify which groups differed significantly, Dunn's post-hoc test with Bonferroni correction was performed. By further examining the means, medians, and boxplots, the direction of the differences was determined. For the UTvAC scale, videos in the 1–5 minute group had statistically significantly lower scores compared to those in the 10–15 minute group and the > 15 minute group. Similarly, for the JAMA scale, videos in the 1–5 minute group had statistically significantly lower scores compared to the 10–15 minute group. For the Modified DISCERN scale, videos in the 5–10 minute group had statistically significantly lower scores compared to those in the > 15 minute group (Table 4 ). Table 4 Comparison of Video Length Groups and Evaluation Scales Kruskal-Wallis rank sum test Variable Chi_Squared Df p value* GQS 4.61 3 0.203 Modified DISCERN 7.98 3 0.046 JAMA 9.54 3 0.023 UTvAC 15.40 3 0.0016 Posthoc Dunn’s test with bonferonni correction Variable Comparison Test_Statistic p value* Modified DISCERN > 5- 15 2.56 0.0317 JAMA > 1- 10- 1- 10- 1- 15 min -2.40 0.0495 *: A significance level of p < 0.05 was considered statistically significant. The correlation between video length (in seconds) and view counts was analyzed using Spearman's correlation , resulting in a Spearman correlation coefficient of -0.0012 . This value is extremely close to 0, indicating a negligible and non-meaningful relationship. The relationship between video length groups and view counts was further examined using the Kruskal-Wallis test, which resulted in a p-value of 0.1171, indicating no statistically significant difference in view counts across the video length groups. Video length showed a weak to moderate positive association with both total comments (Spearman correlation coefficient: 0.2302) and positive comments percentages(positive comments/total comments) (Spearman correlation coefficient: 0.3089), although the relationships were not strong. This suggests that video length might slightly influence viewer engagement. When comparing video length groups with comments (p-value = 0.315) and sentiments, including both positive (p-value = 0.3791) and negative (p-value = 0.5729), using the Kruskal-Wallis test, none of the results were statistically significant. When comparing positive comment percentages with scale scores, the correlations were found to be very weak(Spearman correlation coefficient: GQS: 0.0512, Modified DISCERN: 0.0954, JAMA: 0.0117, UTvAC: 0.1319). While UTvAC scores showed a relatively higher correlation with positive sentiment percentages, the relationship remained weak overall. Discussion This is the first study that analyses ART-related videos on YouTube in terms of different scales and the relationship between these scales and different characteristics of the videos. The descriptive statistics of the videos for analysis are in accordance with the literatüre ( 9 , 10 ). It is an expected finding that videos are generally short. Video producers generally produce short videos. It was observed that some of the videos analysed were directly clinic or doctor advertisements or included advertisements in their content. It is not known whether this situation affects the algorithm and whether it brings videos containing advertisements to the forefront when people search for content. In our study, video quality was evaluated at a moderate level according to the UTvAC. Modified DISCERN scores were between 3 and 4 points, which can be called moderate-good according to this scoring. GQS scores were between 3 and 4, which can be considered as moderate-good useful for patients according to this scoring. Finally, JAMA scores were mostly at 2 points. This indicates that the videos were of low quality. When the data is examined, it is seen that more than 70% of them are individuals or organisations that can be the authority of this issue. It is noteworthy that most of the channel groups are doctor independent influencers and the least of them are in the education category. Of course, this may be related to the opportunities of content producers, the support they receive or their professions. Despite the potential for educational impact, there is a shortage of content specifically categorised under the education category. This gap suggests an opportunity for more structured and authoritative educational content on health topics ( 8 ). It is noteworthy that Education channels have the highest rates in terms of the number of likes, views and comments, although they are few in number among the channel groups. Here, the inclusion of students along with the patients researching the videos probably increases the number of views. There are studies showing that educational videos that are engaging and informative tend to receive more engagement ( 15 , 16 ). The reason why the like/view rate of doctor independent influencers among the expert individuals and institutions is higher than the others may be the effect of doctors' own patients. It is noteworthy that there is no correlation between the channel groups and all scales used, and the results of the scales are similar across channels ( 17 ). In this study, as a finding that we have not encountered in the literature, a syntimetal analysis was performed on user comments. When the comment analysis is analysed, the fact that the rate of positive comments is low reveals that the viewers generally do not like the videos very much and indicate this in the comments. Similar stuides in literatüre, in our study too in general, short video length groups scored lower on the JAMA, Modified DISCERN and UTvAC ( 18 , 19 ). This may be attributed to insufficient information content in short videos. When the relationship between video lengths and video groups and the number of views was analysed, the fact that there was no statistically significant result can be interpreted as the fact that video lengths do not affect the number of views of the video. When have been looked at the studies in the literature, there are studies showing that viewers do not prefer longer videos even if they are of higher quality ( 20 , 21 ). As a finding not found in other studies, when the percentage of positive comments was compared with the scores obtained from the scales, the very low correlation between them revealed that the positive comments of the viewers were not related to the video quality. It was observed that the UTvAC was the most blind to the viewers' comments. Limitations This study has some limitations. First of all, the study was limited to videos published only in English on YouTube. This may lead us to ignore the possibility that videos in other languages ​​may differ in terms of quality and content. In addition, the selected videos were found using only certain keywords; therefore, related videos uploaded with less common or alternative terms may not have been included. The majority of the analyzed videos come from individual creators or small-scale organizations. The limited number of professional videos produced by large healthcare institutions or competent authorities may make it difficult to interpret the results in a broader perspective. Future studies could provide a more comprehensive analysis with a wider range of languages, different platforms, and various assessment tools. Conclucion YouTube has emerged as a significant platform for health-related videos, providing a vast array of content ranging from professional medical advice to personal health experiences ( 7 ). This accessibility allows users to easily find information on various health topics, which can be both beneficial and challenging ( 8 ). On one hand, YouTube can serve as a valuable resource for health education, offering visual and engaging content that can enhance understanding and awareness On the other hand, the platform's open nature means that not all information is reliable or scientifically accurate, posing a risk of misinformation ( 6 , 22 – 24 ). Therefore while YouTube can be a powerful tool for health communication, it is crucial for viewers to critically evaluate the credibility of the sources and the content they consume. In conclusion, when we evaluate ART videos on YouTube, we can say that video content is generally informative for patients. However, users should be careful when searching and should search for content from experts or healthcare organizations that refer to reliable sources for up-to-date information. It is important to focus on newer content due to advancing technologies in ART. Abbreviations ART: Assited Reproductive Techniques IVF: in vitro fertilization IUI: Intrauterine insemination GQS: Global Quality Score JAMA: Journal of American Medical Association benchmark criteria UTvAC: YouTube Video Assessment Criteria Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data needed to evaluate the conclusions are presented in the paper. The data sets generated during and/or analyzed during this study are available from the corresponding author on reasonable request. Competing interests SD and MNG wrote all analyzes and the manuscript. All authors read and approved the final manuscript. Funding Not applicable. Authors' contributions The authors anylized and wrote together on all topics of the article. Acknowledgements The authors would like to express their gratitude to those who uploaded the video for their contribution. References Steptoe PC, Edwards RG. Birth after the reimplantation of a human embryo. Lancet. 1978;312(8085):366. Johnson MH. A short history of in vitro fertilization (IVF). J In Vitro Fert Embryo Transf. 2019;63(3-4-5):83-92. Zegers-Hochschild F, Adamson GD, Dyer S, Racowsky C, De Mouzon J, Sokol R, et al. The international glossary on infertility and fertility care, 2017. Fertil Steril. 2017;32(9):1786-801. Arthurs J, Drakopoulou S, Gandini A. Researching YouTube. Convergence. 2018;24(1):3-15. Globalmediainsight. YouTube Statistics 2024 (Demographics, Users by Country & More). Available from: https://www.globalmediainsight.com/blog/youtube-users-statistics/ Accessed 30 Dec 2024. Osman W, Mohamed F, Elhassan M, Shoufan A. Is YouTube a reliable source of health-related information? A systematic review. J Biomed Eng. 2022;22(1):382. Gabarron E, Fernandez-Luque L, Armayones M, Lau AY. Identifying measures used for assessing quality of YouTube videos with patient health information: a review of current literature. J Med Internet Res. 2013;2(1):e2465. Madathil KC, Rivera-Rodriguez AJ, Greenstein JS, Gramopadhye AK. Healthcare information on YouTube: a systematic review. J Health Informatics J. 2015;21(3):173-94. Kelly-Hedrick M, Grunberg PH, Brochu F, Zelkowitz P. “It’s totally okay to be sad, but never lose hope”: Content analysis of infertility-related videos on YouTube in relation to viewer preferences. J Obstet Gynecol. 2018;20(5):e10199. Ku S, Balasubramanian A, Yu J, Srivatsav A, Gondokusumo J, Tatem AJ, et al. A systematic evaluation of YouTube as an information source for male infertility. J Fertil Steril. 2021;33(6):611-5. Azer SA, AlKhawajah NM, Alshamlan YA. Critical evaluation of YouTube videos on colostomy and ileostomy: Can these videos be used as learning resources? J Patient Educ Counsel. 2022;105(2):383-9. Bernard A, Langille M, Hughes S, Rose C, Leddin D, Van Zanten S. A systematic review of patient inflammatory bowel disease information resources on the World Wide Web. J Can Med Assoc. 2007;102(9):2070-7. Charnock D, Shepperd S, Needham G, Gann R. DISCERN: an instrument for judging the quality of written consumer health information on treatment choices. J Epidemiol Health. 1999;53(2):105-11. Silberg WM, Lundberg GD, Musacchio RA. Assessing, controlling, and assuring the quality of medical information on the Internet: Caveant lector et viewor—Let the reader and viewer beware. JAMA. 1997;277(15):1244-5. Bello-Bravo J, Payumo J, Pittendrigh B. Measuring the impact and reach of informal educational videos on YouTube: The case of Scientific Animations Without Borders. Heliyon. 2021;7(12):e08508. Saurabh S, Gautam S. Modelling and statistical analysis of YouTube's educational videos: A channel owner's perspective. J Comput Educ. 2019;128:145-58. Horani K, Coskey A, Hagedorn JC. Evaluating the Quality of Ankle Fracture Education on Short-Form Video Platform YouTube Shorts. J Orthopaedic Surg. 2023;8(4):2473011423S00304. Aydin MA, Akyol H. Quality of information available on YouTube videos pertaining to thyroid cancer. J Cancer Educ. 2020;35(3):599-605. Duran MB, Kizilkan Y. Quality analysis of testicular cancer videos on YouTube. J Oncol Analysis. 2021;53(8):e14118. Gokcen HB, Gumussuyu G. A quality analysis of disc herniation videos on YouTube. J Neurosci. 2019;124:e799-e804. Askin A, Tosun J. YouTube as a source of information for transcranial magnetic stimulation in stroke: a quality, reliability and accuracy analysis. J Stroke Dis. 2020;29(12):105309. Hasamnis AA, Patil SS. YouTube as a tool for health education. J Health Promotion. 2019;8(1):241. Engebretsen M. The role, impact, and responsibilities of health experts on social media. A focus group study with future healthcare workers. Front in Cancer. 2024;9:1296296. Mohamed F, Shoufan A. Users’ experience with health-related content on YouTube: an exploratory study. BMC Public Health. 2024;24(1):86. Additional Declarations No competing interests reported. Supplementary Files URLsofART.xlsx Cite Share Download PDF Status: Published Journal Publication published 11 Dec, 2025 Read the published version in Journal of Medical and Dental Investigations → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5803338","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462664241,"identity":"c6ee862f-f15f-465f-a3fd-1d54f121064b","order_by":0,"name":"Selim Demirtaş","email":"data:image/png;base64,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","orcid":"","institution":"Mardin Artuklu University","correspondingAuthor":true,"prefix":"","firstName":"Selim","middleName":"","lastName":"Demirtaş","suffix":""},{"id":462664242,"identity":"39b05d30-c24d-4f54-87a2-1bdef9b0d4c3","order_by":1,"name":"Mehmet Nuri Gördük","email":"","orcid":"","institution":"Mardin Artuklu University","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"Nuri","lastName":"Gördük","suffix":""}],"badges":[],"createdAt":"2025-01-10 11:38:06","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-5803338/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-5803338/v2","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.5577/jomdi.e250109","type":"published","date":"2025-12-12T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83513155,"identity":"494d1741-2e9d-4513-9063-0dd457685a3a","added_by":"auto","created_at":"2025-05-22 13:01:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23778,"visible":true,"origin":"","legend":"\u003cp\u003eVideo Counts After Inclusion and Exclusion Criteria\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5971278/v1/6b9d4bdd9311a602f1d7669a.png"},{"id":83513159,"identity":"02de3e0e-15d5-4b9f-bd06-2f3ca73b6b7f","added_by":"auto","created_at":"2025-05-22 13:01:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40411,"visible":true,"origin":"","legend":"\u003cp\u003eWordcloud of keywords of channels\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5971278/v1/b202740d77aa3c69924770c9.png"},{"id":83513160,"identity":"b1e3e605-aebd-4ae2-8208-95e939b89227","added_by":"auto","created_at":"2025-05-22 13:01:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13069,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of length of the videos\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5971278/v1/6252c025cbd3a428c9fd7332.png"},{"id":83513162,"identity":"2ee2510f-6a3b-49cf-861c-ba82a371040b","added_by":"auto","created_at":"2025-05-22 13:09:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37276,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of evaluation scale score results\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5971278/v1/730127010e1cfa931e813f14.png"},{"id":101708340,"identity":"dd7ea8d3-c7af-4c98-ad3a-32fcc16417a1","added_by":"auto","created_at":"2026-02-02 20:33:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":873465,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5803338/v2/5d7b7977-78ef-4899-8a6c-2fbc5d7b3b61.pdf"},{"id":83513152,"identity":"d17b3eec-36d9-4271-9438-0297ddd1defe","added_by":"auto","created_at":"2025-05-22 13:09:36","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":49284,"visible":true,"origin":"","legend":"","description":"","filename":"URLsofART.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5971278/v1/550b4660b585178334c8a183.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Reliability of YouTube as a Patient Education Tool for Assisted Reproductive Technologies","fulltext":[{"header":"Background","content":"\u003cp\u003eAssisted reproductive techniques (ART) have made significant progress in the field of infertility treatment. After the first successful in vitro fertilization (IVF) procedure and the birth of the first \"test tube baby\" named Louise Brown in 1978, ART methods developed rapidly and found a wide range of applications (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Apart from IVF, techniques such as Intrauterine insemination (IUI), intracytoplasmic sperm injection (ICSI) and genetic screening are also widely used in infertility treatment (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Technological innovations in this field have made treatment processes safer, more effective and accessible. While ART applications make individuals' and couples' hopes of having children come true, increasing the level of knowledge and understanding of treatment processes has also gained importance.\u003c/p\u003e \u003cp\u003eFounded in 2005, YouTube has grown rapidly as the internet's largest video sharing platform and has reached billions of users today (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). YouTube has approximately 2.7\u0026nbsp;billion active users worldwide as of 2024. This means that around 52% of internet users worldwide access YouTube at least once every month, and 56.7% of social media users worldwide use YouTube (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). YouTube has become an active source of information not only for entertainment and social content, but also in important areas such as education and health (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In particular, content in the field of health facilitates users\u0026rsquo; access to medical information (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is no exact percentage of YouTube users worldwide who watch health content. However, some studies and data show that health topics are followed by a significant audience on YouTube (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are many videos about ART on YouTube and we could not find any direct study on this subject in the literature. Existing studies examine a specific topic (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In this study, we tried to evaluate how useful and reliable ART videos on YouTube are for patients through four score analyses.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a study of YouTube videos using the YouTube Data API v3 on October 1, 2024. The search was performed with the following keywords: \u0026apos;IVF fertilization\u0026apos;, \u0026apos;infertility treatment\u0026apos;, \u0026apos;male infertility\u0026apos;, \u0026apos;female infertility\u0026apos;, \u0026apos;IVF baby\u0026apos;, \u0026apos;assisted reproductive technology\u0026apos;, and \u0026apos;IVF procedure\u0026apos;.\u003c/p\u003e\n\u003cp\u003eThe YouTube API provided direct access to video metadata, including video ID, title, author, channel description, channel keywords, channel subscribers, publication date, duration, views, total comment count, and likes. The channels were categorized into six groups based on the authors of the videos.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion and Exclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eVideoSelection: For each keyword, the first 50 videos were selected, resulting in an initial pool of 350 videos. Duplicate videos were subsequently removed.\u003c/li\u003e\n \u003cli\u003eVideo Duration: Videos shorter than 60 seconds were excluded to eliminate brief, non-informative clips.\u003c/li\u003e\n \u003cli\u003eRecency: Videos published more than 10 years prior to the search date were excluded to ensure the data reflect recent discussions and up-to-date medical information.\u003c/li\u003e\n \u003cli\u003eComment Analysis: Videos with fewer than 10 comments were excluded, as the research focuses on sentiment analysis of comments.\u003c/li\u003e\n \u003cli\u003eLanguage: Only videos in English were included to ensure consistency and comprehensibility during analysis.\u003c/li\u003e\n \u003cli\u003eFinal Dataset: After applying all exclusion criteria, a total of 76 videos were included in the study (Figure 1).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eComment Retrieval and Preprocessing and Sentiment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the YouTube Data API, we retrieved the top-level comments (i.e., main comments without replies) from each of the selected videos. The focus on top-level comments was to ensure that the analysis captures the primary discussions initiated by viewers, rather than responses, which may not reflect opinions about video but answer to a comment.\u003c/p\u003e\n\u003cp\u003eComments were preprocessed to remove irrelevant elements such as emojis, URLs, special characters, and non-alphanumeric symbols. Additionally, single-character comments (e.g., \u0026quot;!\u0026quot;, \u0026quot;.\u0026quot;, etc.) were excluded from the analysis to enhance the relevance and clarity of the data.\u003c/p\u003e\n\u003cp\u003eSentiment analysis was performed on the cleaned comments to evaluate the tone of viewer discussions. We used the twitter-roberta-base-sentiment model, a transformer-based approach known for accurately capturing nuanced sentiments, including sarcasm and context-specific expressions. Comments were classified as positive, neutral, or negative, and the overall sentiment distribution and mean sentiment score were calculated for each video.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScales\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour different scales were used to evaluate the quality of the videos: Global Quality Score (GQS), modified DISCERN, Journal of American Medical Association (JAMA) benchmark criteria and YouTube Video Assessment Criteria (UTvAC) (11-14). Each video was independently watched and assessed by the authors, and the average score of the authors was considered in the analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs the study utilized publicly available open-source data, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003eAll data collection, extraction, correction, and analysis were performed using Python and R programming languages, along with their relevant libraries.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo visualize the most frequently used keywords in the channel descriptions of the videos, a word cloud was created. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the most prominent keywords include \u003cb\u003eIVF\u003c/b\u003e, \u003cb\u003emedical\u003c/b\u003e, \u003cb\u003enews\u003c/b\u003e, \u003cb\u003efertility\u003c/b\u003e, \u003cb\u003ehealth\u003c/b\u003e, and \u003cb\u003einfertility\u003c/b\u003e, followed by terms like \u003cb\u003edoctor\u003c/b\u003e, \u003cb\u003etreatment\u003c/b\u003e, and \u003cb\u003ebiology\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, 76 YouTube videos were analyzed, and the characteristics outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were examined. After reviewing the videos, 31.6% of them were either direct advertisements or included advertisements alongside informational content.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Video Metrics and Quality Scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubscribers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1636134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3557661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e284000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20700000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViews\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e582062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3155754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays on Youtube\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength(seconds)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGQS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified DISCERN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJAMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUTvAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution of video lengths shows a right-skewed pattern, as most of the videos are concentrated in the 0\u0026ndash;15 minute range ( Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe quality scores (GQS, Modified DISCERN, JAMA, and UTvAC) indicate that most videos tend to fall in the moderate quality range across all scales. GQS and Modified DISCERN scores are distributed similarly, with most videos scoring between 3 and 4 points. The JAMA scores, however, are notably low, with the majority of videos clustered at 2 points. This suggests that the videos generally meet minimal quality standards according to the JAMA criteria. For the UTvAC, the scores are concentrated around 12 points, with fewer videos scoring at the lower or upper extremes. Given that the UTvAC considers 13 points and above as indicative of high-quality videos, this suggests that most videos fall just below the threshold for high quality, reflecting a moderate evaluation of video quality based on this scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eChannels were grouped according to their descriptions and keywords and six different categories were created. Among these categories, unaffiliated doctors (grouped as \u0026ldquo;doctor independent influencers\u0026rdquo;) had the highest representation, followed by non-governmental organizations and News/Media channels. In the analysis conducted according to channel groups, educational channels had the highest mean views, views per day, likes, and comments. However, the \"doctor independent influencer\" group received relatively more likes, although it received fewer views compared to other groups.\u003c/p\u003e \u003cp\u003eThe Kruskal-Wallis test was used to analyze the relationship between the variables in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and the channel groups, and only the comments were found to be statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive summary of variables by channel group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor independent influencer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-government organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNews/media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUniversity/\u003c/p\u003e \u003cp\u003ehospital/ medical organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIndependent influencer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1644.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3668.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1069.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2122.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3113.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7880.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1964.0\u003c/p\u003e \u003cp\u003e\u0026plusmn; 2478.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e828.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1263.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23682.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26356.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViews\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165967.2\u0026thinsp;\u0026plusmn;\u0026thinsp;375118.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267322.3\u0026thinsp;\u0026plusmn;\u0026thinsp;663609.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185843.6\u0026thinsp;\u0026plusmn;\u0026thinsp;445910.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e304539.9\u003c/p\u003e \u003cp\u003e\u0026plusmn; 507272.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138205.2\u0026thinsp;\u0026plusmn;\u0026thinsp;109789.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1693231.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2068319.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViews per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141\u0026thinsp;\u0026plusmn;\u0026thinsp;320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153\u0026thinsp;\u0026plusmn;\u0026thinsp;296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211\u0026thinsp;\u0026plusmn;\u0026thinsp;469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e306\u0026thinsp;\u0026plusmn;\u0026thinsp;747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.2\u0026thinsp;\u0026plusmn;\u0026thinsp;23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e507\u0026thinsp;\u0026plusmn;\u0026thinsp;574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.6\u0026thinsp;\u0026plusmn;\u0026thinsp;125.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.1\u0026thinsp;\u0026plusmn;\u0026thinsp;76.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e564.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1580.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e230.0\u003c/p\u003e \u003cp\u003e\u0026plusmn; 276.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.5\u0026thinsp;\u0026plusmn;\u0026thinsp;142.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e959.0\u0026thinsp;\u0026plusmn;\u0026thinsp;995.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive Comments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.1\u0026thinsp;\u0026plusmn;\u0026thinsp;114.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.6\u0026thinsp;\u0026plusmn;\u0026thinsp;69.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e231.0\u0026thinsp;\u0026plusmn;\u0026thinsp;164.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGQS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified DISCERN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJAMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUTvAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*: The Kruskal-Wallis test was applied, with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eApproximately 9,000 main comments from the videos examined were subjected to sentiment analysis. According to the analysis, 28% of the comments were classified as positive. Comments with positive sentiments received higher scores, and it was understood that the comments grouped as positive by the model were actually highly positive according to the model's evaluation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Sentiment Analysis Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003etype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSentiment mean(sd)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2556(28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81(0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3295(36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70(0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eneutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3287(36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68(0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVideos were categorized into four groups based on their length: \u003cb\u003e1\u0026ndash;5 minutes, 5.01-10 minutes, 10.01-15 minutes\u003c/b\u003e, and \u003cb\u003egreater than 15 minutes.\u003c/b\u003e The relationship between these video length groups and scale scores was analyzed using the \u003cb\u003eKruskal-Wallis test\u003c/b\u003e, as none of the scale scores followed a normal distribution according to the \u003cb\u003eShapiro-Wilk test.\u003c/b\u003e For the Global Quality Score (GQS), no statistically significant difference was observed between the groups. However, for the JAMA, UTvAC, and Modified DISCERN scales, the differences between groups were statistically significantTo identify which groups differed significantly, Dunn's post-hoc test with Bonferroni correction was performed. By further examining the means, medians, and boxplots, the direction of the differences was determined. For the UTvAC scale, videos in the 1\u0026ndash;5 minute group had statistically significantly lower scores compared to those in the 10\u0026ndash;15 minute group and the \u0026gt;\u0026thinsp;15 minute group. Similarly, for the JAMA scale, videos in the 1\u0026ndash;5 minute group had statistically significantly lower scores compared to the 10\u0026ndash;15 minute group. For the Modified DISCERN scale, videos in the 5\u0026ndash;10 minute group had statistically significantly lower scores compared to those in the \u0026gt;\u0026thinsp;15 minute group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Video Length Groups and Evaluation Scales\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eKruskal-Wallis rank sum test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi_Squared\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGQS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified DISCERN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJAMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUTvAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePosthoc Dunn\u0026rsquo;s test with bonferonni correction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eComparison\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTest_Statistic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep value*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModified DISCERN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5-\u0026lt;=10 vs\u0026thinsp;\u0026gt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJAMA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1-\u0026lt;=5 min vs\u0026thinsp;\u0026gt;\u0026thinsp;10-\u0026lt;=15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eUTvAC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1-\u0026lt;=5 min vs\u0026thinsp;\u0026gt;\u0026thinsp;10-\u0026lt;=15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1-\u0026lt;=5 min vs\u0026thinsp;\u0026gt;\u0026thinsp;15 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*: A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eThe correlation between video length (in seconds) and view counts was analyzed using \u003cb\u003eSpearman's correlation\u003c/b\u003e, resulting in a \u003cb\u003eSpearman correlation coefficient of -0.0012\u003c/b\u003e. This value is extremely close to 0, indicating a negligible and non-meaningful relationship. The relationship between video length groups and view counts was further examined using the Kruskal-Wallis test, which resulted in a p-value of 0.1171, indicating no statistically significant difference in view counts across the video length groups. Video length showed a weak to moderate positive association with both total comments (Spearman correlation coefficient: 0.2302) and positive comments percentages(positive comments/total comments) (Spearman correlation coefficient: 0.3089), although the relationships were not strong. This suggests that video length might slightly influence viewer engagement. When comparing video length groups with comments (p-value\u0026thinsp;=\u0026thinsp;0.315) and sentiments, including both positive (p-value\u0026thinsp;=\u0026thinsp;0.3791) and negative (p-value\u0026thinsp;=\u0026thinsp;0.5729), using the Kruskal-Wallis test, none of the results were statistically significant.\u003c/p\u003e \u003cp\u003eWhen comparing positive comment percentages with scale scores, the correlations were found to be very weak(Spearman correlation coefficient: GQS: 0.0512, Modified DISCERN: 0.0954, JAMA: 0.0117, UTvAC: 0.1319). While UTvAC scores showed a relatively higher correlation with positive sentiment percentages, the relationship remained weak overall.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study that analyses ART-related videos on YouTube in terms of different scales and the relationship between these scales and different characteristics of the videos. The descriptive statistics of the videos for analysis are in accordance with the literat\u0026uuml;re (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is an expected finding that videos are generally short. Video producers generally produce short videos. It was observed that some of the videos analysed were directly clinic or doctor advertisements or included advertisements in their content. It is not known whether this situation affects the algorithm and whether it brings videos containing advertisements to the forefront when people search for content.\u003c/p\u003e \u003cp\u003eIn our study, video quality was evaluated at a moderate level according to the UTvAC. Modified DISCERN scores were between 3 and 4 points, which can be called moderate-good according to this scoring. GQS scores were between 3 and 4, which can be considered as moderate-good useful for patients according to this scoring. Finally, JAMA scores were mostly at 2 points. This indicates that the videos were of low quality.\u003c/p\u003e \u003cp\u003eWhen the data is examined, it is seen that more than 70% of them are individuals or organisations that can be the authority of this issue. It is noteworthy that most of the channel groups are doctor independent influencers and the least of them are in the education category. Of course, this may be related to the opportunities of content producers, the support they receive or their professions. Despite the potential for educational impact, there is a shortage of content specifically categorised under the education category. This gap suggests an opportunity for more structured and authoritative educational content on health topics (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is noteworthy that Education channels have the highest rates in terms of the number of likes, views and comments, although they are few in number among the channel groups. Here, the inclusion of students along with the patients researching the videos probably increases the number of views. There are studies showing that educational videos that are engaging and informative tend to receive more engagement (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The reason why the like/view rate of doctor independent influencers among the expert individuals and institutions is higher than the others may be the effect of doctors' own patients. It is noteworthy that there is no correlation between the channel groups and all scales used, and the results of the scales are similar across channels (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, as a finding that we have not encountered in the literature, a syntimetal analysis was performed on user comments. When the comment analysis is analysed, the fact that the rate of positive comments is low reveals that the viewers generally do not like the videos very much and indicate this in the comments.\u003c/p\u003e \u003cp\u003eSimilar stuides in literat\u0026uuml;re, in our study too in general, short video length groups scored lower on the JAMA, Modified DISCERN and UTvAC (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This may be attributed to insufficient information content in short videos. When the relationship between video lengths and video groups and the number of views was analysed, the fact that there was no statistically significant result can be interpreted as the fact that video lengths do not affect the number of views of the video. When have been looked at the studies in the literature, there are studies showing that viewers do not prefer longer videos even if they are of higher quality (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a finding not found in other studies, when the percentage of positive comments was compared with the scores obtained from the scales, the very low correlation between them revealed that the positive comments of the viewers were not related to the video quality. It was observed that the UTvAC was the most blind to the viewers' comments.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has some limitations. First of all, the study was limited to videos published only in English on YouTube. This may lead us to ignore the possibility that videos in other languages ​​may differ in terms of quality and content. In addition, the selected videos were found using only certain keywords; therefore, related videos uploaded with less common or alternative terms may not have been included.\u003c/p\u003e \u003cp\u003eThe majority of the analyzed videos come from individual creators or small-scale organizations. The limited number of professional videos produced by large healthcare institutions or competent authorities may make it difficult to interpret the results in a broader perspective.\u003c/p\u003e \u003cp\u003eFuture studies could provide a more comprehensive analysis with a wider range of languages, different platforms, and various assessment tools.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclucion","content":"\u003cp\u003eYouTube has emerged as a significant platform for health-related videos, providing a vast array of content ranging from professional medical advice to personal health experiences (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This accessibility allows users to easily find information on various health topics, which can be both beneficial and challenging (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). On one hand, YouTube can serve as a valuable resource for health education, offering visual and engaging content that can enhance understanding and awareness On the other hand, the platform's open nature means that not all information is reliable or scientifically accurate, posing a risk of misinformation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Therefore while YouTube can be a powerful tool for health communication, it is crucial for viewers to critically evaluate the credibility of the sources and the content they consume.\u003c/p\u003e \u003cp\u003eIn conclusion, when we evaluate ART videos on YouTube, we can say that video content is generally informative for patients. However, users should be careful when searching and should search for content from experts or healthcare organizations that refer to reliable sources for up-to-date information. It is important to focus on newer content due to advancing technologies in ART.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eART: Assited Reproductive Techniques\u003c/p\u003e\n\u003cp\u003eIVF: in vitro fertilization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIUI: Intrauterine insemination\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGQS: Global Quality Score\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJAMA: \u0026nbsp;Journal of American Medical Association benchmark criteria\u003c/p\u003e\n\u003cp\u003eUTvAC: YouTube Video Assessment Criteria\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data needed to evaluate the conclusions are presented in the paper. The data sets generated during and/or analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSD and MNG wrote all analyzes and the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors anylized and wrote together on all topics of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to those who uploaded the video for their contribution.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSteptoe PC, Edwards RG. Birth after the reimplantation of a human embryo. Lancet. 1978;312(8085):366.\u003c/li\u003e\n\u003cli\u003eJohnson MH. A short history of in vitro fertilization (IVF). J In Vitro Fert Embryo Transf. 2019;63(3-4-5):83-92.\u003c/li\u003e\n\u003cli\u003eZegers-Hochschild F, Adamson GD, Dyer S, Racowsky C, De Mouzon J, Sokol R, et al. The international glossary on infertility and fertility care, 2017. Fertil Steril. 2017;32(9):1786-801.\u003c/li\u003e\n\u003cli\u003eArthurs J, Drakopoulou S, Gandini A. Researching YouTube. Convergence. 2018;24(1):3-15. \u003c/li\u003e\n\u003cli\u003eGlobalmediainsight. YouTube Statistics 2024 (Demographics, Users by Country \u0026amp; More). Available from: https://www.globalmediainsight.com/blog/youtube-users-statistics/ Accessed 30 Dec 2024.\u003c/li\u003e\n\u003cli\u003eOsman W, Mohamed F, Elhassan M, Shoufan A. Is YouTube a reliable source of health-related information? A systematic review. J Biomed Eng. 2022;22(1):382.\u003c/li\u003e\n\u003cli\u003eGabarron E, Fernandez-Luque L, Armayones M, Lau AY. Identifying measures used for assessing quality of YouTube videos with patient health information: a review of current literature. J Med Internet Res. 2013;2(1):e2465.\u003c/li\u003e\n\u003cli\u003eMadathil KC, Rivera-Rodriguez AJ, Greenstein JS, Gramopadhye AK. Healthcare information on YouTube: a systematic review. J Health Informatics J. 2015;21(3):173-94.\u003c/li\u003e\n\u003cli\u003eKelly-Hedrick M, Grunberg PH, Brochu F, Zelkowitz P. \u0026ldquo;It\u0026rsquo;s totally okay to be sad, but never lose hope\u0026rdquo;: Content analysis of infertility-related videos on YouTube in relation to viewer preferences. J Obstet Gynecol. 2018;20(5):e10199.\u003c/li\u003e\n\u003cli\u003eKu S, Balasubramanian A, Yu J, Srivatsav A, Gondokusumo J, Tatem AJ, et al. A systematic evaluation of YouTube as an information source for male infertility. J Fertil Steril. 2021;33(6):611-5.\u003c/li\u003e\n\u003cli\u003eAzer SA, AlKhawajah NM, Alshamlan YA. Critical evaluation of YouTube videos on colostomy and ileostomy: Can these videos be used as learning resources? J Patient Educ Counsel. 2022;105(2):383-9.\u003c/li\u003e\n\u003cli\u003eBernard A, Langille M, Hughes S, Rose C, Leddin D, Van Zanten S. A systematic review of patient inflammatory bowel disease information resources on the World Wide Web. J Can Med Assoc. 2007;102(9):2070-7.\u003c/li\u003e\n\u003cli\u003eCharnock D, Shepperd S, Needham G, Gann R. DISCERN: an instrument for judging the quality of written consumer health information on treatment choices. J Epidemiol Health. 1999;53(2):105-11.\u003c/li\u003e\n\u003cli\u003eSilberg WM, Lundberg GD, Musacchio RA. Assessing, controlling, and assuring the quality of medical information on the Internet: Caveant lector et viewor\u0026mdash;Let the reader and viewer beware. JAMA. 1997;277(15):1244-5.\u003c/li\u003e\n\u003cli\u003eBello-Bravo J, Payumo J, Pittendrigh B. Measuring the impact and reach of informal educational videos on YouTube: The case of Scientific Animations Without Borders. Heliyon. 2021;7(12):e08508.\u003c/li\u003e\n\u003cli\u003eSaurabh S, Gautam S. Modelling and statistical analysis of YouTube\u0026apos;s educational videos: A channel owner\u0026apos;s perspective. J Comput Educ. 2019;128:145-58.\u003c/li\u003e\n\u003cli\u003eHorani K, Coskey A, Hagedorn JC. Evaluating the Quality of Ankle Fracture Education on Short-Form Video Platform YouTube Shorts. J Orthopaedic Surg. 2023;8(4):2473011423S00304.\u003c/li\u003e\n\u003cli\u003eAydin MA, Akyol H. Quality of information available on YouTube videos pertaining to thyroid cancer. J Cancer Educ. 2020;35(3):599-605.\u003c/li\u003e\n\u003cli\u003eDuran MB, Kizilkan Y. Quality analysis of testicular cancer videos on YouTube. J Oncol Analysis. 2021;53(8):e14118.\u003c/li\u003e\n\u003cli\u003eGokcen HB, Gumussuyu G. A quality analysis of disc herniation videos on YouTube. J Neurosci. 2019;124:e799-e804.\u003c/li\u003e\n\u003cli\u003eAskin A, Tosun J. YouTube as a source of information for transcranial magnetic stimulation in stroke: a quality, reliability and accuracy analysis. J Stroke Dis. 2020;29(12):105309.\u003c/li\u003e\n\u003cli\u003eHasamnis AA, Patil SS. YouTube as a tool for health education. J Health Promotion. 2019;8(1):241.\u003c/li\u003e\n\u003cli\u003eEngebretsen M. The role, impact, and responsibilities of health experts on social media. A focus group study with future healthcare workers. Front in Cancer. 2024;9:1296296.\u003c/li\u003e\n\u003cli\u003eMohamed F, Shoufan A. Users\u0026rsquo; experience with health-related content on YouTube: an exploratory study. BMC Public Health. 2024;24(1):86.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ART, Youtube, GQS, JAMA, DISCERN, UTvAC","lastPublishedDoi":"10.21203/rs.3.rs-5803338/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5803338/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAssisted reproductive techniques (ART) have made significant progress in infertility treatment and have found a wide range of applications with various methods, especially in vitro fertilisation. Today, digital platforms such as YouTube have become an important resource for individuals seeking information about ART. However, uncertainties about the reliability and quality of this content pose a potential risk for viewers. This study aims to assess the quality and reliability of ART-related videos on YouTube.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e76 videos were analyzed by applying elimination criteria from 350 videos uploaded between October 1, 2014 and October 1, 2024. These videos were obtained through YouTube Data API v3 by searching seven different terms related to in vitro fertilization and infertility. In addition to descriptive statistics on video features, the Global Quality Scale (GQS), Modified DISCERN, JAMA and UTvAC scales were applied to evaluate the quality of the videos. Additionally, sentiment analysis was conducted on the comments of the videos.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIt was determined that the majority of the analyzed videos were at medium quality level. While the JAMA scores indicate that most of the videos are of low quality, the GQS and Modified DISCERN scores suggest that most videos fall within the medium quality range. According to UTvAC, most videos are just below the high-quality limit. The majority of videos were uploaded by doctor-independent influencers (32.9%), non-governmental organizations (25.0%), and news/media (21.1%) channels. The positive comment percentage was found to be 28%, indicating that the videos were generally poorly appreciated by viewers.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study revealed that the information quality and reliability of ART videos on YouTube are generally moderate. It is recommended that viewers should adopt a critical approach and turn to reliable sources when evaluating content on ART. The findings of the study show that there is a significant need for improvements in digital health communication and educational content production.\u003c/p\u003e","manuscriptTitle":"Assessing the Reliability of YouTube as a Patient Education Tool for Assisted Reproductive Technologies","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-05-22 13:01:31","doi":"10.21203/rs.3.rs-5803338/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2025-01-15 11:48:28","doi":"10.21203/rs.3.rs-5803338/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a0944c1-e2ba-4cc5-9b32-4a83d134a611","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-05T14:13:16+00:00","versionOfRecord":{"articleIdentity":"rs-5803338","link":"https://doi.org/10.5577/jomdi.e250109","journal":{"identity":"journal-of-medical-and-dental-investigations","isVorOnly":true,"title":"Journal of Medical and Dental Investigations"},"publishedOn":"2025-12-12 00:00:00","publishedOnDateReadable":"December 12th, 2025"},"versionCreatedAt":"2025-05-22 13:01:31","video":"","vorDoi":"10.5577/jomdi.e250109","vorDoiUrl":"https://doi.org/10.5577/jomdi.e250109","workflowStages":[]},"version":"v2","identity":"rs-5803338","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5803338","identity":"rs-5803338","version":["v2"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}