Search Results (5)

Introduction: In this narrative review, we identified concepts related to the process of transforming a psychiatry curriculum for New Zealand medical students. Method: A literature search was performed on four databases (Embase, Medline, PsycInfo, and Scopus) for articles related to curriculum development in psychiatry, including relevant aspects of culture and technology. Results: Ninety-three articles met the inclusion criteria. Three main themes were identified: the needs of learners; curriculum frameworks that optimise learning; and the role of technology. The key features of an effective psychiatry curriculum are the extent to which it integrates with other disciplines, develops key competencies, supports authentic learning, and promotes cultural safety. Conclusions: Transforming curricula is an iterative process that prioritises learners’ needs, establishes psychiatry within the teaching context, integrates learning evidence, and responds to the changing demands of society. The findings from this review apply to medical curricula more generally: a well-integrated specialist curriculum, in this case psychiatry, enables medical students to build essential competencies and depends upon effective collaboration with stakeholders, attention to cultural safety, and incorporating technology into the teaching context. Full article

Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and mental health needs globally. Increasingly, passively acquired data from wearables are augmented with carefully selected active data from depressed individuals to develop Machine Learning (ML) models of depression based on mood scores. However, most ML models are black box in nature, and hence the outputs are not explainable. Depression is also multimodal, and the reasons for depression may vary significantly between individuals. Explainable and personalised models will thus be beneficial to clinicians to determine the main features that lead to a decline in the mood state of a depressed individual, thus enabling suitable personalised therapy. This is currently lacking. Therefore, this study presents a methodology for developing personalised and accurate Deep Learning (DL)-based predictive mood models for depression, along with novel methods for identifying the key facets that lead to the exacerbation of depressive symptoms. We illustrate our approach by using an existing multimodal dataset containing longitudinal Ecological Momentary Assessments of depression, lifestyle data from wearables and neurocognitive assessments for 14 mild to moderately depressed participants over one month. We develop classification- and regression-based DL models to predict participants’ current mood scores—a discrete score given to a participant based on the severity of their depressive symptoms. The models are trained inside eight different evolutionary-algorithm-based optimisation schemes that optimise the model parameters for a maximum predictive performance. A five-fold cross-validation scheme is used to verify the DL model’s predictive performance against 10 classical ML-based models, with a model error as low as 6% for some participants. We use the best model from the optimisation process to extract indicators, using SHAP, ALE and Anchors from explainable AI literature to explain why certain predictions are made and how they affect mood. These feature insights can assist health professionals in incorporating personalised interventions into a depressed individual’s treatment regimen. Full article

Recent clinical trials suggest that psychedelic-assisted therapy is a promising intervention for reducing anxiety and depression and ameliorating existential despair in advanced cancer patients. However, little is known about perceptions toward this treatment from the key gatekeepers to this population. The current study aimed to understand the perceptions of cancer healthcare professionals about the potential use of psychedelic-assisted therapy in advanced cancer patients. Twelve cancer healthcare professionals including doctors, nurses, psychologists and social workers took part in a semi-structured interview which explored their awareness and perceptions toward psychedelic-assisted therapy with advanced cancer patients. Data were analysed using thematic analysis. Four inter-connected themes were identified. Two themes relate to the role and responsibility of being a cancer healthcare worker: (1) ‘beneficence: a need to alleviate the suffering of cancer patients’ and (2) ‘non-maleficence: keeping vulnerable cancer patients safe’, and two themes relate specifically to the potential for psychedelic-assisted therapy as (3) ‘a transformative approach with the potential for real benefit’ but that (4) ‘new frontiers can be risky endeavours’. The findings from this study suggest intrigue and openness in cancer healthcare professionals to the idea of utilising psychedelic-assisted therapy with advanced cancer patients. Openness to the concept appeared to be driven by a lack of current effective treatment options and a desire to alleviate suffering. However, acceptance was tempered by concerns around safety and the importance of conducting rigorous, well-designed trials. The results from this study provide a useful basis for engaging with healthcare professionals about future research, trial design and potential clinical applications. Full article

This commentary presents commonalities in medically unexplained symptoms (MUS) across multiple organ systems, including symptoms, aetiological mechanisms, comorbidity with mental health disorders, symptom burden and impact on quality of life. Further, treatment outcomes and barriers in the clinician–patient relationship, and cross-cultural experiences are highlighted. This discussion is necessary in aiding an improved understanding and management of MUS due to the interconnectedness underlying MUS presentations across the spectrum of medical specialties. Full article

Volunteers in non-government organisations are increasingly providing mental health support due to increasing demand and in the context of overstretched publicly-funded mental health services. This descriptive, cross-sectional study explored a knowledge gap in the literature of mental health telephone counselling by examining the motivation and retention determinants of helpline volunteers. In total, 25 participants were recruited across four focus groups and five individual interviews from a non-government organisation which provides a national phone counselling service to callers in New Zealand. Interviews were electronically recorded, transcribed and thematically analysed. Volunteers were found to have a high regard for their role and enjoyed many aspects including initial training, ongoing supports (formal/informal) and nature of the phone calls. However, organisational priorities/communication, disconnectedness, technological issues, lack of recognition and lack of a sense of belonging were reasons cited for intention to leave but previous mental health experiences, autonomy/flexibility, self-discovery/skills development and being there for someone else were key factors for volunteers to start and remain in their role. Understanding these crucial factors may help modulate volunteer satisfaction and retention in mental health organisations but may also potentially be relevant to other types of volunteer organisations. Full article