The introduction of case-based learning (CBL) by the School of Medicine at Cardiff University has encouraged innovation in medical teaching and learning. During years one and two of the modernized MBBCh program, students complete 17 cases as part of the newly developed C21 curriculum that emphasizes a patient-oriented and student-centered approach to learning. The mental health case, which is presented in year 2, incorporates a number of novel teaching resources that aim to enhance the students' learning experience and to further reinforce the patient-oriented and community-based philosophy of C21. These include the use of fictionalized video diaries, virtual patient cases, e-learning workbooks, an interactive practical session, and community placements. Novel teaching methods and resources were evaluated by students in terms of effectiveness and value as learning resources through the administration of a structured mixed questionnaire. The results revealed that students valued the inclusion of these resources, which they evaluated as having contributed to their understanding of the subject area. Furthermore, the case was found to have had an impact on student interest in psychiatry as a specialty as well as a career choice. The positive student evaluation of this case supports the innovations in teaching delivery inspired by C21.

Background Since the COVID-19 outbreak was declared a global pandemic, public health messages have emphasised the importance of frequent handwashing in limiting the transmission of the virus. Whilst crucial in controlling transmission, such messaging may have an adverse effect on individuals with OCD. Methods A cross-sectional study was conducted, with a total of 332 participants recruited. Participants who scored above the optimal cut-off score on the Obsessive-Compulsive Inventory Revised edition (OCI-R) were included in the analysis (n = 254). Scores on the six subscales of the OCI-R were correlated with responses to a COVID-19 Impact measure. Results Factor analysis of the COVID-19 Impact measure revealed that items loaded on two components of the measure (handwashing and distress-avoidance). Canonical correlation analyses revealed significant associations between the OCI-R subscales and COVID-19 Impact measure, F (12, 490) = 8.14, p = 0.001, and the SHAI subscales with the COVID-19 Impact Measure, F (4, 498) = 8.18, p = 0.001). Specifically, washing and checking OCI-R subscales correlated with both components of the COVID-19 Impact measure, as did the health anxiety and beliefs SHAI subscales. Content analysis revealed disruption to treatment delivery and worsening symptom severity in participants with contamination-related OCD. Discussion Contamination and checking OCD subtypes have been associated with increased hand-washing behaviour and avoidance of distress-inducing cues. Consideration should be given to targeted support tailored to patients with these subtypes of OCD.

Whilst the use of various blended learning models preceded the COVID-19 pandemic, the abrupt shift to remote delivery served as catalyst within the sector in enhancing digital solutions to meet immediate student needs. As we emerge from the pandemic, a return to purely didactic and impersonal in-person teaching seems anticlimactic, with the return to the lecture theatre seeing many lecturers trialling various digital tools in creating more interactive in-person, synchronous, and asynchronous sessions. In evaluating students' experiences of the various tools and approaches applied by academic staff, a survey was developed by a multidisciplinary team of educators at Cardiff University's School of Medicine exploring student perceptions of e-learning resources (ELRs), as well as student experiences of various blended learning approaches. The primary aim of this study was to evaluate student experience, satisfaction, and engagement with ELRs and blended learning. A total of 179 students (undergraduate and postgraduate) completed the survey. 97% confirmed that e-learning resources were blended within the teaching they received, with 77% rating the quality of e-learning as good-to-excellent and 66% reporting a preference for asynchronous resources that enable them to learn at their own pace. A variety of platforms, tools, and approaches were identified by students as meeting their diverse learning needs. We therefore propose a personalised, evidencebased and inclusive learning (PEBIL) model enabling the application of digital technologies both on and offline.

Background Major depressive disorder is a common mental disorder affecting 5% of adults worldwide. Early contact with health care services is critical for achieving accurate diagnosis and improving patient outcomes. Key symptoms of major depressive disorder (depression hereafter) such as cognitive distortions are observed in verbal communication, which can also manifest in the structure of written language. Thus, the automatic analysis of text outputs may provide opportunities for early intervention in settings where written communication is rich and regular, such as social media and web-based forums. Objective The objective of this study was 2-fold. We sought to gauge the effectiveness of different machine learning approaches to identify users of the mass web-based forum Reddit, who eventually disclose a diagnosis of depression. We then aimed to determine whether the time between a forum post and a depression diagnosis date was a relevant factor in performing this detection. Methods A total of 2 Reddit data sets containing posts belonging to users with and without a history of depression diagnosis were obtained. The intersection of these data sets provided users with an estimated date of depression diagnosis. This derived data set was used as an input for several machine learning classifiers, including transformer-based language models (LMs). Results Bidirectional Encoder Representations from Transformers (BERT) and MentalBERT transformer-based LMs proved the most effective in distinguishing forum users with a known depression diagnosis from those without. They each obtained a mean F1-score of 0.64 across the experimental setups used for binary classification. The results also suggested that the final 12 to 16 weeks (about 3-4 months) of posts before a depressed user’s estimated diagnosis date are the most indicative of their illness, with data before that period not helping the models detect more accurately. Furthermore, in the 4- to 8-week period before the user’s estimated diagnosis date, their posts exhibited more negative sentiment than any other 4-week period in their post history. Conclusions Transformer-based LMs may be used on data from web-based social media forums to identify users at risk for psychiatric conditions such as depression. Language features picked up by these classifiers might predate depression onset by weeks to months, enabling proactive mental health care interventions to support those at risk for this condition.

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