The Efficacy and Therapeutic Alliance of Augmented Reality Exposure Therapy in Treating Adults With Phobic Disorders: Systematic Review.

BACKGROUND: Phobic disorders are characterized by excessive fear of a stimulus that can affect the quality of a patient's life. The lifetime prevalence in adults is 7.7% to 12.5%. The current literature provides evidence-based inferences about the effectiveness of in-vivo exposure therapy (IVET) in treating phobia. However, this method can put the therapist and the client in danger, with high drop out and refusal rates. A newer approach for exposure therapy using augmented reality technology is under assessment. OBJECTIVE: This systematic review investigated the novel technology's efficacy, cost-efficacy, and therapeutic alliance in treating adults with phobia. METHODS: An extensive search was conducted using 4 major databases (MEDLINE, PsycINFO, Embase, and Scopus) using a comprehensive list of synonyms for augmented reality exposure therapy (ARET) and phobic disorders. The search targeted any randomized control trial testing ARET in adults with phobic disorders up to August 8, 2022. RESULTS: A total of 6 studies were included, with 208 participants providing results. Studies investigating the efficacy of ARET compared to no intervention showed significant results (P<.05) in the ARET group improvement. Head-to-head comparative studies comparing ARET to IVET showed no significant difference (P>.05) in the effectiveness and therapeutic alliance between both therapies. Further, the results demonstrated that the ARET group had a better long-term effect than IVET, with the ability to put the patients in more situations to face the feared object. CONCLUSIONS: The current data suggest clinically significant efficacy and a promising therapeutic alliance of ARET. However, no data are available investigating the cost-effectiveness of ARET. Further research is warranted to ascertain ARET's cost-effectiveness and examine its efficacy in other populations and anxiety conditions.

Enabling Early Health Care Intervention by Detecting Depression in Users of Web-Based Forums using Language Models: Longitudinal Analysis and Evaluation.

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.

Developing a personalised, evidence-based and inclusive learning (PEBIL) model of blended learning: A cross-sectional survey.

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, evidence-based and inclusive learning (PEBIL) model enabling the application of digital technologies both on and offline.

Medical students observing a primary care consultation: does student gender affect patient consent?

Clinical placement is an important aspect of undergraduate education in the United Kingdom (UK) but with no national curriculum for primary care teaching, it is important to consider the learning opportunities afforded to students when on these rotations. In earlier years, observing consultations constitutes a large proportion of student experience, with patient consent an integral aspect of this teaching method. This study investigated whether patients consider the gender of a medical student when granting consent for their primary care appointment to be observed and whether this was conditional based on their presenting complaint. In total, 551 adult participants (420 females and 131 males) residing in the UK, aged 18-87 years, responded to an online questionnaire. In total, 229 (41.6%) participants stated that they would be influenced by the student's gender when consenting to observation, notably if the presenting complaint concerned an intimate area or their sexual health. A statistically significant correlation was revealed for consent, participant age and participant gender, with younger female respondents less likely to consent to observation by male students. The findings highlight a potential concern pertaining to equal opportunities between medical students based on gender, with inclusivity and diversity considerations for medical schools and clinicians.

Investigating the Association Between Obsessive-Compulsive Disorder Symptom Subtypes and Health Anxiety as Impacted by the COVID-19 Pandemic: A Cross-Sectional Study.

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.

A case-based medical curriculum for the 21st century: The use of innovative approaches in designing and developing a case on mental health.

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.

Avoidance and behavioural flexibility in obsessive compulsive disorder.

Three experiments measured differences in responding between participants scoring either higher or lower on obsessive-compulsive trait measures. A Sidman avoidance procedure was employed in Experiment 1, in which participants were required to identify an avoidance response that postponed an aversive event, and noted that higher scorers maintained this response more successfully. Experiments 2 and 3 involved an operant variability procedure to differentiate between variable and rigid responding among participants demonstrating high versus low obsessive-compulsive traits, and revealed no differential sensitivity to rigid responding between the groups. The results provide insight into the nature of obsessive-compulsive behavioural traits, suggesting that avoidance but not stereotypy is primary in OCD.