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This study aims to examine the scope and contribution of Forensic Clinical Psychology (FCP) advice from the National Crime Agency (NCA) to criminal investigations in the UK to address the gap in current knowledge and research.

The 36 FCP reports reviewed were written between 2017 and 2021. They were analysed using Toulmin’s (1958) application of pertinent arguments to the evaluation process. The potential utility of the reports was analysed in terms of the advice provided.

Most of the reports involved murder and equivocal death. The reports focused primarily on understanding the offender’s psychopathology, actions, motivation and risk to self and others using a practitioner model of case study methodology. Out of the 539 claims, grounds were provided for 99% of the claims, 91% had designated modality, 62% of the claims were potentially verifiable and 57% of the claims were supported by a warrant and/or backing. Most of the reports provided either moderate or high insight into the offence/offender (92%) and potential for new leads (64%).

The advice provided relied heavily on extensive forensic clinical and investigative experience of offenders, guided by theory and research and was often performed under considerable time pressure. Flexibility, impartiality, rigour and resilience are essential prerequisites for this type of work.

To the best of the authors’ knowledge, this study is the first to systematically evaluate forensic clinical psychology reports from the NCA. It shows the pragmatic, dynamic and varied nature of FCP contributions to investigations and its potential utility.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

This paper aims to explore the potential utility of Identity Structure Analysis (ISA) in single-case and group-level outcome and process evaluations.

A study was conducted to evaluate mentalization-based therapy by using ISA and its linked framework software, Ipseus. Ten patients with borderline personality disorder and substance use disorder were involved in the study. ISA/Ipseus was administered prior to and at the completion of the treatment. Five-year follow-up data, comprising behavioural indicators, were also collected and compared to ISA/Ipseus results.

Improvements occurred in the evaluation of stressful, demanding and emotionally burdening situations. Evaluations on concerned others also improved, together with progress in self-reflection. Changes in the evaluation of recovery-related themes were less salient. On a case level, changes in the self-states and role models were consistent with the results of the five-year-follow up data. An initial crisis state seems suggestive of progress, while initial defensive positions with high positive self-regard, of stagnation.

ISA/Ipseus, integrating the benefits of qualitative and quantitative approaches in evaluation, is a potential method to explore the complexity of identity changes during therapy.

This study aims to evaluate the quality of information recorded in Behaviour Monitoring Charts (BMC) for Behaviours that Challenge (BtC) in dementia in an older adult inpatient dementia service in the North of England (Aim I) and to understand staff perceptions and experiences of completing BMC for BtC in dementia (Aim II).

Descriptive statistics and graphs were used to analyse and interpret quantitative data gathered from BMC (Aim I) and Likert-scale survey responses (Aim II). Thematic analysis (Braun and Clarke, 2006) was used to analyse and interpret qualitative data collected from responses to open-ended survey questions and, separately, focus group discussions (Aim II).

Analysis of the BMCs revealed that some of the data recorded relating to antecedents, behaviours and consequences lacked richness and used vague language (i.e. gave reassurance), which limited its clinical utility. Overall, participants and respondents found BMC to be problematic. For them, completing BMCs were not viewed as worthwhile, the processes that followed their completion were unclear, and they left staff feeling disempowered in the systemic hierarchy of an inpatient setting.

Functional analysis of BMC helps identify and inform appropriately tailored interventions for BtC in dementia. Understanding how BMCs are used and how staff perceive BMC provides a unique opportunity to improve them. Improving BMC will support better functional analysis of BtC, thus allowing for more tailored interventions to meet the needs of people with dementia.

This paper contributes to a dialogue about the psychoanalytic concept of free association and its application in the context of qualitative research interviewing. In doing so, it also adds to wider discussion regarding the relationship between clinical psychoanalysis, psychoanalytic psychotherapy and qualitative research.

Critical consideration of different perspectives on the application of free association in the qualitative research interview, extending earlier work addressing this issue. Differences and similarities in the way the concept of free association is articulated are examined regarding its framing in psychoanalysis and psychoanalytically oriented psychotherapy.

Whether researchers see themselves as borrowing, applying or drawing inspiration from free association, there is scope for muddling distinct ways of viewing it as it is conceived in psychoanalysis.

Considerations are outlined for researchers interested in psychoanalytically informed methods to be mindful of.

Maladaptive daydreaming (MD) has yet to be recognized as a formal condition. This paper aims to shed light on the phenomenon of daydreaming, its potential maladaptive nature and the characteristics of MD, as well as potential interventions that may be implemented to address it.

The present paper is a general conceptual review of the condition of MD. It provides a historical overview of the phenomenon and attempts to draw meaningful inferences from the scientific work pertaining to the development of diagnostic criteria, the assessment and interventions developed to treat MD.

Studies have shown that MD can cause distress and impair an individual's typical functioning, and specific diagnostic criteria and symptoms have been identified. Scheduled clinical interviews, self-report measures and derivative treatment modules are currently utilized to understand, assess and treat the symptoms related to MD.

Formal recognition of the condition ensures that the individuals receiving treatment for the condition are provided with insurance coverage and reimbursement for treatment.

Authors also hope for MD recognition, awareness, reduced stigma and acceptance.

This review offers a fair overview of the recent scientific findings pertaining to MD and attempts to open a channel of discourse to enhance the inclusivity of relevant psychopathological conditions in the existing classifications.

Today, e-government (electronic government) applications have extended to the frontiers of health-care delivery. E-Nabız contains personal health records of health services received, whether public or private. The use of the application by patients and physicians has provided efficiency and cost advantages. The success of e-Nabız depends on the level of technology acceptance of health-care service providers and recipients. While there is a large research literature on the technology acceptance of service recipients in health-care services, there is a limited number of studies on physicians providing services. This study aims to determine the level of influence of trust and privacy variables in addition to performance expectancy, effort expectancy, social influence and facilitating factors in the unified theory of acceptance and use of technology (UTAUT) model on the intention and behavior of using e-Nabız application.

The population of the study consisted of general practitioners and specialist physicians actively working in any health facility in Turkey. Data were collected cross-sectionally from 236 physicians on a voluntary basis through a questionnaire. The response rate of data collection was calculated as 47.20%. Data were collected cross-sectionally from 236 physicians through a questionnaire. Descriptive statistics, correlation analysis and structural equation modeling were used to analyze the data.

The study found that performance expectancy, effort expectancy, trust and perceived privacy had a significant effect on physicians’ behavioral intentions to adopt the e-Nabız system. In addition, facilitating conditions and behavioral intention were determinants of usage behavior (p < 0.05). However, no significant relationship was found between social influence and behavioral intention (p > 0.05).

This study confirms that the UTAUT model provides an appropriate framework for predicting factors influencing physicians’ behaviors and intention to use e-Nabız. In addition, the empirical findings show that trust and perceived privacy, which are additionally considered in the model, are also influential.

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.

This paper aims to evaluate the effect of consumption of a high-fat diet (HFD) rich with total saturated fats on adiposity and serum levels of vascular cell adhesion molecule (sVCAM-1), a biomarker of endothelial inflammation/dysfunction. Another aim is to evaluate whether supplementation of a phytosomal formulation of curcumin would reduce adiposity measures and sVCAM-1 levels in HFD rats.

The study was conducted on 17 male rats which were allocated to one of three feeding regimen groups: normal diet (ND); HFD, or HFD with dietary phytosomal curcumin (HFD-C). Anthropometric measures were recorded weekly up to 20 weeks of feeding intervention, at the end of which, sVCAM-1 levels were also compared with one-way ANOVA and Tukey post-hoc analysis.

The HFD group had the greatest values for raw anthropometric data, and there was a group difference in anthropometric measures, however there was no significant difference between HFD and HFD-C for any measure. The gain at 20 weeks from initial values did reveal significant differences in weight and abdominal circumference between HFD and HFD-C groups. There were significant group differences in sVCAM-1 levels, with only HFD-C displaying significant lower levels than HFD group.

This is the first study that shows the capacity of a phytosomal formulation of curcumin in reducing adiposity and sVCAM-1 levels during daily intake of saturated fats above the recommended level. The results are promising in that this formulation can protect against endothelial inflammation/dysfunction, and can be used as complimentary therapy to suppress dyslipidemia/obesity-related cardiovascular complications.

Nutrition labels provide a cost-effective method of conveying nutrition information to consumers. This study aimed to assess the use of nutrition facts panels, knowledge of traffic light labelling (TLL) and perceived healthiness of food items using TLL among consumers.

A web-based cross-sectional study was conducted among adults in the United Arab Emirates (UAE) (n = 1,322). TLL knowledge score was derived for each participant. Conjoint analysis was used to calculate the utilities and relative importance of the perceived healthiness scores for four attributes (fat, saturated fat, total sugar, salt) at the aggregate level.

Participants had a positive attitude towards TLL but were less familiar with TLL than the nutrition facts panel (47.4 vs 85.8%). The mean TLL knowledge score was 3.6 out of 7 (51.6%). Younger age, higher education, higher income, and health-related qualifications were associated with higher scores. Conjoint analysis showed that participants tend to choose products with greener labels, especially for sugars (80.1%) and avoid red labels for fats. Sugars had the highest percentage value of relative importance compared to the other attributes (27.1%).

The study outcomes offer valuable insights into the extent of consumer awareness, comprehension and utilization of nutrition facts panels in the UAE. These findings contribute essential knowledge for a deeper understanding of the impact of nutrition labels on consumer behaviour and decision-making in the region.