A qualitative analysis of female Reddit users' experiences with low libido: how do women perceive their changes in sexual desire?

BACKGROUND: The true prevalence of low sexual desire among women is disputed among researchers due to the complex nature and presentation of women's sexual problems. AIM: To qualitatively analyze the aspects of libido/sexual desire frequently discussed by Reddit users and compare them with the current understanding of female sexual well-being and sexual desire disorders. METHODS: By using the Reddit application programming interface, the TwoXChromosomes subreddit was queried for posts with the keywords libido and sex drive. Posts that were deleted or unrelated to themes of libido/sex drive were excluded. A total of 85 threads-63 queried from the keyword libido and 22 from sex drive-and approximately 2900 comments were qualitatively analyzed per the grounded theory approach. Five independent researchers read and coded each thread to identify dominant themes and emergent concepts. OUTCOMES: Outcomes of interest included codes related to sexual dysfunction, libido, orgasm, masturbation, types of sex, psychology, relationships, intimacy, treatment, medications, and health care. RESULTS: Posters were primarily heterosexual women in their 20s and 30s. The code categories with the highest frequency were relationships (22.7%, n = 272), libido (22.2%, n = 210), psychological (20.2%, n = 191), medications (7.29%, n = 69), and intimacy (6.0%, n = 57). Users frequently described a decrease in libido secondary to medications, particularly antidepressants and hormonal birth control. Many users discussed the challenges of navigating a relationship with low sexual desire and the resulting sexual distress. Posters described feelings of sadness, anxiety, and guilt due to their low desire. Additionally, users discussed the role that sex plays in relationships, whether as a way to develop intimacy between partners or to achieve orgasm. Finally, posters expressed dissatisfaction with health care addressing their concerns surrounding sexual desire. CLINICAL IMPLICATIONS: The study findings-namely, the impact of medications on sexual health, the interaction of sexual desire and mental health, and cited examples of inadequate sexual health care-can help guide sexual well-being research, diagnosis, and public policy. STRENGTHS AND LIMITATIONS: Using Reddit as a data source allowed for the analysis of women's experiences outside the preestablished concepts of female sexual desire. Limitations to the study include the potential for posts to be deleted by moderator guidelines, the young demographic distribution of Reddit users, and the popularity-based structure of subreddit threads. CONCLUSION: Our results emphasize the psychosocial aspects of sexual desire and the need to redefine sexual problems to encompass the complex nature of female sexual well-being.

#Coronavirus on TikTok: user engagement with misinformation as a potential threat to public health behavior.

Coronavirus disease (COVID)-related misinformation is prevalent online, including on social media. The purpose of this study was to explore factors associated with user engagement with COVID-related misinformation on the social media platform, TikTok. A sample of TikTok videos associated with the hashtag #coronavirus was downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, and high) using a codebook developed by experts in infectious diseases. Multivariable modeling was used to evaluate factors associated with number of views and presence of user comments indicating intention to change behavior. One hundred and sixty-six TikTok videos were identified and reviewed. Moderate misinformation was present in 36 (22%) videos viewed a median of 6.8 million times (interquartile range [IQR] 3.6-16 million), and high-level misinformation was present in 11 (7%) videos viewed a median of 9.4 million times (IQR 5.1-18 million). After controlling for characteristics and content, videos containing moderate misinformation were less likely to generate a user response indicating intended behavior change. By contrast, videos containing high-level misinformation were less likely to be viewed but demonstrated a nonsignificant trend towards higher engagement among viewers. COVID-related misinformation is less frequently viewed on TikTok but more likely to engage viewers. Public health authorities can combat misinformation on social media by posting informative content of their own.

"O" no: a Reddit analysis of orgasmic dysfunction.

Background: Female Reddit users frequently discussed potential causes of orgasm difficulties and its implications on mental health and relationships. Aim: This study aimed to evaluate the experiences of women discussing orgasms on the Internet site Reddit. We sought to qualitatively analyze the topics that arose in users' discussions to better understand the potential causes of orgasm difficulties and its implications on quality of life. Methods: Posts on the subreddit r/TwoXChromosomes containing the keywords "orgasm" and "climax" were included in the dataset. Posts and their associated comments were qualitatively analyzed using the grounded theory approach. Two independent researchers coded each thread to identify dominant themes and emergent concepts. Outcomes: The most frequently coded primary topics included: (1) orgasm (32.2% [n = 337]), (2) psychological (17.8% [n = 186]), (3) relationships (15.4% [n = 161]), and (4) treatment (10.7% [n = 112]). Results: Qualitative analysis of 107 threads and approximately 6300 comments resulted in 5 major categories: psychological aspect of orgasms, difficulty orgasming with partners, partners' responses to orgasmic dysfunction, types of orgasms, and treatments for orgasmic dysfunction. Preliminary themes included (1) the presence of an emotional component or history of trauma related to orgasmic difficulty, (2) difficulty orgasming with a partner regardless of ability to orgasm during masturbation and a variety of stimulation required to orgasm, (3) mixed partner responses to orgasmic dysfunction, (4) the definition of a normal orgasm, and (5) self-motivated treatment for orgasmic dysfunction, including clitoral stimulation devices and masturbation techniques. Notably, few posters discussed their orgasmic dysfunction with healthcare providers. Clinical Translation: The study reveals insights into the possible causes, psychosocial implications, and treatment of orgasm difficulties from a patient perspective, and can guide future research on female orgasms in a more precise, patient-oriented direction. Strengths and Limitations: The anonymous nature of the forum allowed for insight into sensitive topics related to female orgasms and sexual trauma. Limitations include the demographic distribution of Reddit users, which was primarily younger women in their 20s and 30s, which restricts generalizability. Conclusion: Reddit provides a medium for individuals with orgasm difficulties to discuss their experiences. Posts addressed users' inability to orgasm, their mental health and relationships, the stimulation required for orgasm, and treatments for orgasmic dysfunction. Interestingly, very few posts discussed healthcare, potentially suggesting that women do not classify their orgasmic dysfunction as a health issue.

Exploring Urological Malignancies on Pinterest: Content Analysis.

BACKGROUND: Pinterest is a visually oriented social media platform with over 250 million monthly users. Previous studies have found misinformative content on genitourinary malignancies to be broadly disseminated on YouTube; however, no study has assessed the quality of this content on Pinterest. OBJECTIVE: Our objective was to evaluate the quality, understandability, and actionability of genitourinary malignancy content on Pinterest. METHODS: We examined 540 Pinterest posts or pins, using the following search terms: "bladder cancer," "kidney cancer," "prostate cancer," and "testicular cancer." The pins were limited to English language and topic-specific content, resulting in the following exclusions: bladder (n=88), kidney (n=4), prostate (n=79), and testicular cancer (n=10), leaving 359 pins as the final analytic sample. Pinterest pins were classified based on publisher and perceived race or ethnicity. Content was assessed using 2 validated grading systems: DISCERN quality criteria and the Patient Education Materials Assessment Tool. The presence of misinformation was evaluated using a published Likert scale ranging from 1=none to 5=high. RESULTS: Overall, 359 pins with a total of 8507 repins were evaluated. The primary publisher of genitourinary malignancy pins were health and wellness groups (n=162, 45%). Across all genitourinary malignancy pins with people, only 3% (n=7) were perceived as Black. Additionally, Asian (n=2, 1%) and Latinx (n=1, 0.5%) individuals were underrepresented in all pins. Nearly 75% (n=298) of the pins had moderate- to poor-quality information. Misinformative content was apparent in 4%-26% of all genitourinary cancer pins. Understandability and actionability were poor in 55% (n=198) and 100% (n=359) of the pins, respectively. CONCLUSIONS: On Pinterest, the majority of the urological oncology patient-centric content is of low quality and lacks diversity. This widely used, yet unregulated platform has the ability to influence consumers' health knowledge and decision-making. Ultimately, this can lead to consumers making suboptimal medical decisions. Moreover, our findings demonstrate underrepresentation across many racial and ethnic groups. Efforts should be made to ensure the dissemination of diverse, high-quality, and accurate health care information to the millions of users on Pinterest and other social media platforms.

Automatically Assessing Quality of Online Health Articles.

Today Information in the world wide web is overwhelmed by unprecedented quantity of data on versatile topics with varied quality. However, the quality of information disseminated in the field of medicine has been questioned as the negative health consequences of health misinformation can be life-threatening. There is currently no generic automated tool for evaluating the quality of online health information spanned over broad range. To address this gap, in this paper, we applied data mining approach to automatically assess the quality of online health articles based on 10 quality criteria. We have prepared a labelled dataset with 53012 features and applied different feature selection methods to identify the best feature subset with which our trained classifier achieved an accuracy of [Formula: see text] varied over 10 criteria. Our semantic analysis of features shows the underpinning associations between the selected features & assessment criteria and further rationalize our assessment approach. Our findings will help in identifying high quality health articles and thus aiding users in shaping their opinion to make right choice while picking health related help from online.

A Large-Scale Analysis of Health Journalism by Reliable and Unreliable Media.

Media outlets play crucial roles in disseminating health information. Previous studies have examined how health journalism is practiced by reliable and unreliable media outlets. However, most of the existing works are conducted over a relatively small set of samples. In this study, we investigate a large collection (about 30 thousand) of health-related news articles which were published by 29 reliable and 20 unreliable media outlets and identify several differences in health journalism practice. Our analysis shows that there are significant structural, topical, and semantic disparities in the way reliable and unreliable media outlets conduct health journalism. We argue, in this age of 'fake news', these findings will be useful to combat online health disinformation.

Classification of cancer cells using computational analysis of dynamic morphology.

BACKGROUND AND OBJECTIVE: Detection of metastatic tumor cells is important for early diagnosis and staging of cancer. However, such cells are exceedingly difficult to detect from blood or biopsy samples at the disease onset. It is reported that cancer cells, and especially metastatic tumor cells, show very distinctive morphological behavior compared to their healthy counterparts on aptamer functionalized substrates. The ability to quickly analyze the data and quantify the cell morphology for an instant real-time feedback can certainly contribute to early cancer diagnosis. A supervised machine learning approach is presented for identification and classification of cancer cell gestures for early diagnosis. METHODS: We quantified the morphologically distinct behavior of metastatic cells and their healthy counterparts captured on aptamer-functionalized glass substrates from time-lapse optical micrographs. As a proof of concept, the morphologies of human glioblastoma (hGBM) and astrocyte cells were used. The cells were captured and imaged with an optical microscope. Multiple feature vectors were extracted to quantify and differentiate the complex physical gestures of cancerous and non-cancerous cells. Three different classifier models, Support Vector Machine (SVM), Random Forest Tree (RFT), and Naïve Bayes Classifier (NBC) were trained with the known dataset using machine learning algorithms. The performances of the classifiers were compared for accuracy, precision, and recall measurements using five-fold cross-validation technique. RESULTS: All the classifier models detected the cancer cells with an average accuracy of at least 82%. The NBC performed the best among the three classifiers in terms of Precision (0.91), Recall (0.9), and F1-score (0.89) for the existing dataset. CONCLUSIONS: This paper presents a standalone system built on machine learning techniques for cancer screening based on cell gestures. The system offers rapid, efficient, and novel identification of hGBM brain tumor cells and can be extended to define single cell analysis metrics for many other types of tumor cells.