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The authors propose a blockchain platform for managing clinical trial data to enhance data validity, integrity, trust and transparency in the pharmaceutical research process. The authors also provide an extensive review of how blockchain technology supports the business processes of clinical trials.

A systematic literature review was conducted to identify the existing applications of blockchain in pharmaceutical process management. A conceptual design for a blockchain infrastructure to address clinical trial challenges is developed by outlining the entire clinical trial value chain and identifying the coordination and communication among its stakeholders. A stakeholder analysis is conducted to ensure that the clinical trial processes satisfy the requirements and preferences of each stakeholder.

The proposed blockchain platform offers a promising solution for enhancing integrity, trust and transparency in the clinical trial process. Additionally, blockchain can help streamline communication and collaboration between stakeholders by enabling multiple parties to access and share data in real time, lowering the possibility of delays or errors in data analysis and reporting.

The proposed blockchain platform can benefit patients by empowering them to have better-controlled access to their data and by allowing researchers to maintain adherence to reporting requirements. Additionally, the platform can benefit granting agencies, researchers and decision-makers by ensuring the integrity of clinical trial data and streamlining communication and collaboration between stakeholders.

This study builds on existing blockchain applications in pharmaceutical process management by developing a blockchain framework that can address clinical trial concerns from an integrated perspective.

Psychotherapy and clinical supervision outcomes are influenced by client and supervisee factors, one of which is cultural identity. Those with diverse racial and ethnic minoritised identities often experience disparities in therapy outcomes. Therapists and supervisors need to be responsive to the identity of those they support. The multicultural orientation (MCO) framework is an emerging concept in psychotherapy and clinical supervision that may offer these practitioners a framework to be responsive.

A preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews was conducted. Six databases, PubMed, Scopus, Embase, Academic Search Complete, Web of Science and PsychInfo, were searched for peer-reviewed literature published in English between the years 2000 and 2023.

A total of 1,553 sources were identified, of which (n = 42) are included in this review. Findings suggest that MCO is still in its infancy as applied to therapy and clinical supervision. Most of the research has been conducted in America, using quantitative methodologies with white western populations. Cultural humility is the most studied MCO pillar, and variables such as reductions in psychological stress, the working alliance and microaggressions are reported on as outcomes. MCO applied to the group therapy process is an emerging finding of interest. However, more research is needed, especially experiential designs across different and diverse populations and contexts.

MCO is an emerging therapy and clinical supervision process that has the potential to improve the outcomes for therapy clients and supervisees. Further research is needed to replicate current studies, and further research with diverse populations, countries and contexts should be undertaken as a priority.

From the authors’ personal and professional experiences, they have observed that autistic women are uniquely at risk of interpersonal trauma. Given the tendency for autistic women to be overlooked in research and practice, this study aims to rectify this by exploring the relevant literature and including the voices of autistic women throughout this paper.

This study completed a literature review of quantitative and qualitative data relating to exposure to interpersonal trauma in autistic women. This study also reviewed relevant discursive evidence available on in memoirs and reports. This study also included dialogue between us as authors from an auto/“Autie”-ethnographic position.

Both clinical literature and discursive evidence support the idea that autistic women are uniquely at risk of interpersonal trauma, in particular, sexual victimisation. Explanatory factors are considered. Studies exploring rates of post-traumatic stress disorder (PTSD) were less consistent. Further evidence is required to better understand how autistic women experience and express PTSD and to inform assessment and treatment modifications.

To the best of the authors’ knowledge, this is the first paper to integrate clinical literature and discursive evidence on the topic of interpersonal trauma in autistic women. It provides useful insights into the experiences of autistic women in this space, directions for urgently needed future research and modifications to clinical practice.

Why is it that highly trained and seasoned executives fail? On the surface, this doesn’t make sense because they are very successful; yet research in the organization sciences provides no shortage of evidence to prove just that. From the classic Mann Gulch fire disaster of Weick’s famous collapse of sensemaking study, to studies of myopia of learning, escalation of commitment, threat-rigidity, dominant logic, the architecture of simplicity, the Icarus Paradox, to core competencies turning into core rigidities, and navigating new competitive markets using “old” cognitive maps, and many more such examples point to a ubiquitous phenomenon where highly trained and experienced professionals find themselves “stuck” in the heat of battle, unable to move and progress. On the one hand, for some, there is a desperate need for change, but are unable to do so, due to their trained incapacities. On the other hand, some simply cannot see the need for change, and continue with their “business as usual” mentality. For both, their visions of the world shrink, they have a tendency to cling onto their past habitual practices and oversimplify the complexity of the situation. In moments like these: DROP YOUR TOOLS and UNLEARN! This book chapter introduces a framework (grounded in clinical psychology) that has had consistent success in helping seasoned executives and key decision-makers open up the alternatives whenever they find themselves stuck with complexity.

Adoption of Clinical Decision Support Systems (CDSS) is a crucial step towards the digital transition of the healthcare sector. This review aims to determine and synthesise the influential factors in CDSS adoption in inpatient healthcare settings in order to grasp an understanding of the phenomenon and identify future research gaps.

A systematic literature search of five databases (Medline, EMBASE, PsycINFO, Web of Science and Scopus) was conducted between January 2010 and June 2023. The search strategy was a combination of the following keywords and their synonyms: clinical decision support, hospital or secondary care and influential factors. The quality of studies was evaluated against a 40-point rating scale.

Thirteen papers were systematically reviewed and synthesised and deductively classified into three main constructs of the Technology–Organisation–Environment theory. Scarcity of papers investigating CDSS adoption and its challenges, especially in developing countries, was evident.

This study offers a summative account of challenges in the CDSS procurement process. Strategies to help adopters proactively address the challenges are: (1) Hospital leaders need a clear digital strategy aligned with stakeholders' consensus; (2) Developing modular IT solutions and conducting situational analysis to achieve IT goals; and (3) Government policies, accreditation standards and procurement guidelines play a crucial role in navigating the complex CDSS market.

To the best of the authors’ knowledge, this is the first review to address the adoption and procurement of CDSS. Previous literature only addressed challenges and facilitators within the implementation and post-implementation stages. This study focuses on the firm-level adoption phase of CDSS technology with a theory refining lens.

In this chapter, the author critically examines the deeply entrenched practices and theories within counselor education, revealing their roots in historically dominant, Eurocentric, and often racially oppressive assumptions. This study brings to light the pervasive impact of these traditional approaches, illuminating their role in perpetuating racial oppression and disparities in mental health care. The author presents a compelling argument for adopting Critical Race Theory (CRT) as an effective pedagogical and clinical practice framework in the counseling profession, a step toward its much-needed liberation. CRT's tenets are examined as a robust alternative, promoting socially just outcomes in counseling and psychotherapy. The article highlights CRT's capacity to address the well-established relationship between racism, white supremacy, and minority mental health. It proposes a groundbreaking model for praxis, predicated on CRT, which holds potential not only to challenge and disrupt oppressive structures but also to pave the way for the liberation of both the oppressed and the oppressor. This seminal work prompts a re-envisioning of counselor education, asserting a call for a transformative shift toward a liberation-based, social justice pedagogy.

Many psychiatrists and trainees in Aotearoa New Zealand used telepsychiatry during COVID-19 lockdowns, despite minimal experience and training in the area. Research on a culturally safe telepsychiatry framework is lacking in Aotearoa. This study aims to provide a better understanding of telepsychiatry in the Aotearoa context and identify potential gaps with the current practice, with a focus on exploring telepsychiatry use with Maori, Pacific peoples and Asians.

This qualitative study was guided by the principles of the Kaupapa Maori methodology and the “Give Way Rule” from Pan-Pacific studies, to ensure culturally appropriate analysis and outcomes. Semi-structured interviews were conducted with a sample of psychiatrists and trainees recruited from Aotearoa members of the Royal Australian and New Zealand College of Psychiatrists. The qualitative data were then analysed using general inductive thematic analysis to identify the major themes.

In total, 18 participants were interviewed. Three key themes were identified: cultural safety such as preparation for a telepsychiatry session, cultural practices and equity issues; clinical practice such as continuity of care, patient selection and limitations; and process of running a telepsychiatry service.

The analysis of the main themes gives both practical ideas for providing a culturally safe telepsychiatry appointment, as well as a wider base for developing a telepsychiatry service that works particularly for Maori, Pacific and Asians mental health users. Issues around resources and expertise in the field are lacking and further frameworks to support infrastructure and training are needed.

Service users’ voice is at the forefront of movements within psychiatry that look to create more humanising care. Although genuine co-production of knowledge is limited by the power differential intrinsically functional to the health care setting, the arts have the potential to create collaborative environments and equalise relationships. The purpose of this case study is to describe and discuss the design and pilot evaluation of creative writing workshops in a forensic mental health ward as an innovative method for humanising care.

A creative writing intervention focussing on everyday experiences was implemented in a forensic mental health ward and involved four residents and four mental health professionals working together. Interviews were conducted with the four mental health professionals as part of a service evaluation. Transcripts were analysed using thematic analysis.

Two themes emerged from the analysis of interviews with mental health professionals: “a new way of learning about each other” and “imagining beyond the staff-resident relationship”. The authors discuss the intervention’s benefits in terms of its potential to foster mutuality and empathy beyond the illness narrative.

Creative writing can be used to engage patients and mental health professionals to jointly share everyday experiences and identities beyond illness.

The creative writing workshops present an innovative approach concerning the use of creative arts for humanising care through mutuality.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

Breast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction.

The first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level.

The authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection.

The authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.