Vaginitis Videos on YouTube: Quality and Patient Education Analysis

Introduction

Vaginitis is caused by infection, inflammation, or an imbalance in the normal vaginal flora.1,2 Common symptoms include odor, irritation, burning sensation, pruritus, dysuria, dyspareunia, or changes in vaginal discharge.1,2 A proper examination with appropriate diagnostic testing is crucial to differentiate vaginitis from other potential causes of vaginal symptoms, such as vulvar, vaginal, or cervical cancers.3

Vaginitis may become a chronic condition requiring continuous treatment.4 Given its multifactorial etiology and the fact that diagnosis and treatment vary according to the underlying pathogen, consultation with a gynecologist is essential.1,5 In untreated or persistent cases, patients often seek information from alternative sources.

The internet offers broad access to various online medical and audiovisual educational materials. YouTube, one of the most widely used internet-based visual information and entertainment platforms, receives over 2 billion video views per day. Although YouTube includes highly informative content published by professionals, its open-access nature allows the dissemination of potentially misleading or inaccurate information, as it does not verify the credibility of video creators.6,7

Materials and Methods

A YouTube search was performed on December 26, 2025, using the keywords “vaginitis” and “vaginal infection.”

Inclusion criteria for videos were: English language, primarily vaginitis-related content, and acceptable audiovisual quality. Exclusion criteria included non-English language, absence of audio or visual components, and duplicate videos.

To minimize algorithmic bias from prior viewing history, a newly created YouTube account was used. Videos were sorted by view count. For each search term, 45 videos among the top 100 results (ranked by relevance) were selected. A total of 55 videos were excluded (non-English = 32, no audio/visual content = 13, duplicates = 10). Additionally, 2 more videos were identified using the keyword “vaginal infection,” resulting in a final total of 47 videos included in the analysis.

Video Evaluation

For each video, the following parameters were recorded: number of views, video length (minutes), total number of likes, content purpose, and content type.

Videos were categorized into four groups based on their content:

1. Explanatory (providing medical information on vaginitis, including diagnosis, symptoms, and treatment),

2. Treatment procedures (demonstrating or explaining treatment steps in detail),

3. Personal experiences (sharing individual stories and emotional experiences related to vaginitis), and

4. Other (complementary approaches such as nutrition or exercise).

Videos were also classified based on the source of upload into five main groups:

• Academics (affiliated with a university),

• Physicians (medical doctors not affiliated with a university),

• Patients (women diagnosed with and currently or previously treated for vaginitis),

• Commercial organizations (content promoting a product or service),

• Paramedical professionals (such as physical therapists, dietitians, or allied health providers).

Uploads by academics and physicians were grouped as medical, while those from patients, commercial sources, and paramedical professionals were grouped as non-medical.

Since there is no standardized method for evaluating video quality, a scoring system based on a previous study was adopted.8,9

Evaluation parameters included:

• Overall video quality,

• Inclusion and clarity of essential information about vaginitis,

• Level of scientific evidence referenced.

Each parameter was rated on a 3-point scale:

• 1 = Poor,

• 2 = Moderate,

• 3 = Good.

Information related to vaginitis was divided into five components (etiology, symptoms, diagnosis, treatment, and recovery), and scored as follows:

• 0 = Not mentioned,

• 1 = Briefly mentioned,

• 2 = Explained in detail.

Scientific evidence was evaluated across two sub-items:

• 0 = Not mentioned,

• 1 = Mentioned.

The total possible quality score for each video ranged from 2 to 18 points. Each video was independently evaluated by three physicians, and the average of their scores was used in the final analysis.

To assess video popularity, we calculated:

• Like ratio = likes × 100 / total likes,

• View ratio = total views / days since upload,

• Video Power Index (VPI) = (like ratio × view ratio) / 100.

Although previous studies incorporated dislike counts into evaluation metrics, YouTube no longer displays this data due to platform policy changes.

Statistical Analysis

Data on video characteristics such as source, purpose, and post-publication view counts were collected. Continuous variables were presented as median (range) and categorical variables as n (%). Comparisons between medical and non-medical groups were made using the Mann–Whitney U test. Differences in content distribution between medical and non-medical sources were analyzed using Fisher’s exact test. Statistical analyses were conducted using SPSS software (version 25.0, IBM, Armonk, NY, USA). A p-value <0.05 was considered statistically significant.

Inter-rater reliability for the quality scoring across the three physician raters was assessed using intraclass correlation coefficients (ICC).

Results

Among the 47 videos included in the study, the most common content category was "disease explanation," accounting for 72.3% (n = 34) of all videos (Table 1). This was followed by “personal experiences” at 10.6% (n = 5), “other” content such as dietary and lifestyle recommendations or advertisements at 12.8% (n = 6), and “preventive strategies” at 4.3% (n = 2).

Regarding uploader profiles, physicians were the most frequent uploaders, contributing to 46.8% (n = 22) of the videos, followed by academics (27.7%, n = 13), commercial sources (12.8%, n = 6), patients (8.5%, n = 4), and paramedical professionals (4.3%, n = 2) (Table 2).

The mean number of views was 420,813.5±1,610,245.7, with a median of 55,337 (range: 581.6–11,041,478). The mean number of likes was 7,625.8±42,902.2 (median 402; range: 11–295,000). The average video duration was 7.61±6.53 minutes (median 5.00; range: 1.00–27.00). The average view rate per 1000 was 318.04±1,094.29 (median 36.75; range: 0.43–7,037.27). The mean Video Power Index (VPI) was 5.26±27.37 (median 0.33; range: 0.00–188.02), and the mean quality score was 8.96±3.99 (median 8.00; range: 2–18). Descriptive statistics are presented in Table 3.

Comparison of professional (physician, academic, paramedic) versus non-professional (commercial, patient) uploaders using the Mann–Whitney U test is shown in Table 4. The mean VPI was 6.06±30.80 in the professional group and 2.29±4.53 in the non-professional group, with no statistically significant difference (p = 0.4509). However, the mean quality score was significantly higher in the professional group (9.70±4.03) compared to the non-professional group (6.20±2.39) (p = 0.0135).

The highest number of videos was uploaded in 2023 (23.4%; n = 11), followed by 2021 (17.0%; n = 8) and 2022 (12.8%; n = 6). The oldest video dated back to 2010, and the most recent videos were from 2024. The year-wise distribution is shown in Table 5.

Videos were then grouped based on upload time: before the end of 2020 (n = 21) and 2021 and later (n = 26) (Table 6). Median view count was higher in the pre-2021 group (61,645; range: 6,316–11,041,478) than the post-2020 group (39,274.5; range: 582–1,106,650), though the difference was not statistically significant (p = 0.195). Median video duration was similar between groups (8.5 vs. 4.5 minutes; p = 1.000). There were no significant differences in number of likes or view rate (p>0.05). However, the median VPI was significantly higher in the post-2020 group (0.556 vs. 0.114; p = 0.003). The proportion of medical content was similar in both periods (71.4% vs. 76.9%; p = 0.744), and quality scores did not significantly differ (p = 0.083).

Discussion

The finding that 72.3% of the analyzed videos focused on disease definition and symptom explanation suggests that health-related content on social media prioritizes cognitive-level information. This aligns with previous systematic reviews indicating that YouTube health videos are often limited to introductory disease content. For example, a review published in BMC Medical Education emphasized the frequent absence of preventive and practical guidance in such videos.7 While this reinforces the "knowledge acquisition" aspect of cognitive learning theories, it also reveals a lack of applied guidance necessary for self-management and behavioral change.10

When examining source credibility and quality indicators, a positive correlation between professional content creators and higher video quality has been consistently reported in the literature. Videos uploaded by experts have been shown to score significantly higher on GQS and DISCERN metrics.11,12 A recent study in Nature Scientific Reports highlighted the potential of large language models to assess the quality of medical videos with enhanced speed and consistency; however, human expertise remains the gold standard.13 In contrast, videos from non-professional sources pose a higher risk of misinformation, which may undermine viewer trust.14

Regarding engagement metrics and content quality, the weak correlation observed between view count, likes, VPI, and quality scores suggests that superficial popularity measures may not reliably reflect the educational accuracy of health videos. Similar findings have been reported in the domain of physical training videos, where viewer interaction did not guarantee high-quality explanatory content.15 This engagement-quality mismatch has also been documented in gastroenterology education, prompting recommendations that algorithms prioritize content standards rather than numerical popularity alone.10

In terms of time trends and algorithmic bias, the significantly higher median VPI among videos uploaded after 2021 suggests that YouTube's recommendation algorithms prioritize engagement-driving content, regardless of informational quality. A study published in Journal of Medical Internet Research emphasized that algorithmic filtering tends to favor popularity over educational optimization.13 Furthermore, recent reports by the eSafety Commission acknowledged YouTube’s role in health education while stressing the importance of algorithmic oversight to ensure content safety, particularly for children and adolescents.13

Growing evidence supports the effectiveness of video-based health education in promoting behavior change. Systematic reviews have shown that structured interventions can significantly improve viewer health literacy.16 Moreover, deep learning–based tools have been proposed as a means to provide real-time quality feedback to content creators and platform moderators, as suggested in a recent Scientific Reports article.13 These tools have the potential to enhance both scalability and consistency, contributing to the standardization of content quality.

This study revealed that vaginitis-related YouTube videos predominantly focus on symptom explanation and basic information, while guidance on preventive strategies and self-care is notably lacking. Video quality was positively associated with professional involvement, with expert-generated content scoring significantly higher on GQS and DISCERN compared to non-professional sources.11,17 In contrast, engagement metrics such as views, likes, and VPI did not consistently reflect content quality, indicating that algorithmic prioritization of popularity may jeopardize informational reliability.10,15

Temporal trends showed that post-2021 content was more likely to be promoted based on engagement rather than educational merit, underscoring that visibility does not equate to quality.18 Therefore, health education videos must strike a balance between viewer appeal and medical accuracy. Automatic evaluation tools based on deep learning and large language models could offer real-time feedback to creators, thereby promoting standardization and enhancing reliability.13,19

Limitations

Limiting the analysis to the top 100 videos and the inherent subjectivity in manual scoring may affect the generalizability of the results. Future studies should consider using a greater number of raters and combining expert evaluation with automated quality assessment tools to enhance objectivity.

Conclusion

Based on these findings, several strategies could improve vaginitis education on YouTube by making content both more useful and more trustworthy: creators should expand practical, self-management–oriented information by addressing hygiene, prevention, and clear “when to seek urgent care” guidance.7; greater involvement and collaboration of physicians and academics may enhance medical accuracy and strengthen viewer trust.11,12; at the platform level, recommendation algorithms could be optimized to incorporate independent quality indicators rather than relying primarily on engagement metrics13; and, finally, scalable monitoring systems using LLMs and deep learning approaches may support automated quality assessment and help maintain content credibility over time.13,19 Implementing these steps could reinforce YouTube’s public health education value while increasing access to accurate, current, and comprehensive information for viewers.

Statement of ethics

This study did not involve human subjects directly. However, expert opinions were obtained via an online survey. Participation was voluntary, and informed consent was implied by completion of the survey. Ethics committee approval was not required due to the non-interventional nature of the study.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Author contributions

Mücahit Furkan Balcı and Celal Akdemir contributed to the study conception and design. Fatih Yıldırım were responsible for data collection and evaluation. Celal Akdemir conducted the statistical analyses. All authors contributed to drafting the manuscript and approved the final version.