Background Depression is a common disorder that still remains underdiagnosed and undertreated in the UK National Health Service. Charities and voluntary organizations offer mental health services, but they are still struggling to promote these services to the individuals who need them. By analyzing social media (SM) content using machine learning techniques, it may be possible to identify which SM users are currently experiencing low mood, thus enabling the targeted advertising of mental health services to the individuals who would benefit from them. Objective This study aimed to understand SM users’ opinions of analysis of SM content for depression and targeted advertising on SM for mental health services. Methods A Web-based, mixed methods, cross-sectional survey was administered to SM users aged 16 years or older within the United Kingdom. It asked participants about their demographics, their usage of SM, and their history of depression and presented structured and open-ended questions on views of SM content being analyzed for depression and views on receiving targeted advertising for mental health services. Results A total of 183 participants completed the survey, and 114 (62.3%) of them had previously experienced depression. Participants indicated that they posted less during low moods, and they believed that their SM content would not reflect their depression. They could see the possible benefits of identifying depression from SM content but did not believe that the risks to privacy outweighed these benefits. A majority of the participants would not provide consent for such analysis to be conducted on their data and considered it to be intrusive and exposing. Conclusions In a climate of distrust of SM platforms’ usage of personal data, participants in this survey did not perceive that the benefits of targeting advertisements for mental health services to individuals analyzed as having depression would outweigh the risks to privacy. Future work in this area should proceed with caution and should engage stakeholders at all stages to maximize the transparency and trustworthiness of such research endeavors.

Anatomical education has suffered from reduced teaching time and poor availability of staff and resources over the past thirty years. Clay-based modeling (CBM) is an alternative technique for teaching anatomy that can improve student knowledge and experience. This systematic review aimed to summarize and appraise the quality of the literature describing the uses, advantages, and limitations of CBM compared to alternative methods of teaching human gross anatomy to students or qualified healthcare professionals. A systematic search of Embase, MEDLINE, Scopus, and Web of Science databases was conducted, and the Medical Education Research Quality Instrument (MERSQI) was used to assess study quality. Out of the 829 studies identified, 12 papers met the inclusion criteria and were eligible for this review. The studies were of high quality, with a mean MERSQI score of 11.50/18. Clay-based modeling can be used to teach all gross anatomical regions, and 11 studies demonstrated a significant improvement in short-term knowledge gain in students who used CBM in comparison to other methods of learning anatomy. Eight studies that included subjective assessment showed that CBM is rated highly. However, some studies showed that students viewed CBM as juvenile and experienced difficulty making the models. Additionally, there is no evidence to suggest that CBM improves long-term knowledge. Clay-based modeling is an effective learning method for human gross anatomy and should be incorporated into the anatomists' toolkit. In the future, more randomized controlled studies with transparent study designs investigating the long-term impact of CBM are needed. Anat Sci Educ 0: 1-11.

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