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Autism spectrum conditions (ASC) and borderline personality disorder (BPD) have overlapping symptom profiles. Dialectical behaviour therapy (DBT) is an established treatment for self-harm and BPD, but little research has investigated the outcomes of DBT for ASC populations. This exploratory service evaluation aims to investigate the outcomes of a comprehensive DBT programme for adolescents with a diagnosis of emerging BPD and a co-occurring ASC diagnosis as compared to those without an ASC diagnosis.

Differences from the start to end of treatment in the frequency of self-harming behaviours, BPD symptoms, emotion dysregulation, depression, anxiety, the number of A&E attendances and inpatient bed days, education and work status, and treatment non-completion rates were analysed for those with an ASC diagnosis, and compared between those with an ASC diagnosis and those without.

Significant medium to large reductions in self-harming behaviours, BPD symptoms, emotion dysregulation and inpatient bed days were found for those with an ASC diagnosis by the end of treatment. There were no significant differences between those with an ASC and those without in any outcome or in non-completion rates. These findings indicate that DBT may be a useful treatment model for those with an ASC diagnosis, though all results are preliminary and require replication.

To the best of the authors’ knowledge, this is the first study to report the outcomes of a comprehensive DBT programme for adolescents with an ASC diagnosis, and to compare the changes in outcomes between those with a diagnosis and those without.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

This paper makes a conceptual contribution by intersecting two strands of literature (return to work following health issues and industrial relations) to facilitate our understanding of the potential role of social dialogue in supporting return to work (RTW) following the diagnosis of a chronic illness. It conceptualises the levels and channels through which various actors and their interactions may play a role in RTW facilitation within the actor-centred institutional framework.

The paper uses an exploratory design based mainly on desk research but is also informed by roundtable discussions done in six countries as part of a larger project.

The conceptual and analytical framework (CAF) is developed to explain how various actors interact together in ways shaped by the RTW policy framework and the industrial relations systems, resulting in a continuum of RTW facilitation situations.

There is limited research on return-to-work policies following diagnosis of chronic illness from a comprehensive actor-oriented perspective. The existing literature usually focusses on just one stakeholder, overlooking the role of social dialogue actors. By bridging the two streams of literature and incorporating all potential actors and their interactions in a unitary model, the proposed framework provides a valuable tool to further discuss how successful RTW after a diagnosis of chronic illness can be facilitated.

This paper aims to evaluate the quality of co-production between lived experience practitioners (LXPs) and professionals in an interactive National Health Service webinar series aimed at supporting people who were diagnosed or identified with borderline personality disorder.

Transcripts from the webinars were subjected to mixed-method examination combining Foucauldian discourse analysis (FDA) and content analysis (CA).

FDA identified nine discursive objects: diagnosis beyond its medical context, diagnosis as a total explanation, being the other, universality, compassion, hope, faking it, mentalisation and co-production. CA demonstrated those nine discursive objects each corresponded with equalised airtime appropriated by professionals and lived experience practitioners.

The sample was limited and if applied to other mental health settings might reveal different findings. More needs to be understood about the attitudes of professionals and LXPs that support discourse sharing. Although this study has offered evidence of the quality of co-production, it can say very little about whether the co-productive approach offers superior outcomes to other forms of treatment.

Further research could employ FDA and CA to further explore how co-production is being enacted in other situations, with different models, where comparable interventions are delivered. Future research could compare outcomes between co-productive and professional-only interventions.

This study examined naturalistic practice to build new theory in an under-researched area for a substantial mental health population.

This study aims to explore the information needs of Chinese parents of children with Autism Spectrum Disorder (ASD) and how these needs evolve as their children develop.

This study collated 17,122 questions regarding raising children with ASD via the Yi Lin website until November 2021.

The information needs of parents of children with ASD were classified into two categories: 1) Cognition-motivation: related to children with ASD; and 2) Affection-motivation: related to their parents. Child development causes the adaptation of information needs of these parents. Within the first three years, nine different topics of these parents' information needs were identified. Major information needs at this stage are as follows: intervention content, intervention methods and pre-diagnosis questions. During the ages of three to six years, there were 13 topics of information needs for parents, focusing on three areas: intervention content, intervention methods and diagnosis and examination. There are eight topics of information needs post six years. Parents are more concerned with the three topics of intervention content, life planning and intervention methods.

This novel study indicates the complex and changing information needs of parents of children with ASD in China. It may enhance the understanding of the information needs of these parents at theoretical and practical levels, provide support for them to understand their own information needs and provide a reference for relevant government and social organisations to provide targeted information services for them.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2022-0247

Literature regarding recovery has focussed on diagnoses such as schizophrenia, with few papers focussing on borderline personality disorder (BPD). This is a significant area in need of change because a lack of research concentrating on recovery from BPD could be seen to perpetuate the view that recovery from this condition may not be possible. Recovery Colleges (RCs) in the UK began in 2009and aim to offer co-produced and co-facilitated psychoeducational courses to encourage recovery and enable people to develop skills and knowledge so they become experts in the self-management of their difficulties. Given the gaps within the recovery literature, it is unclear how Recovery Colleges can support recovery for people diagnosed with BPD. The purpose of this study was to explore the impact of a Recovery College course for people diagnosed with BPD.

Using participatory methods, this paper aims to explore the question of what personal recovery looks like for people with BPD and how this may prove useful in developing future practice in RCs. Qualitative feedback data was collected from 51 managing intense emotions courses delivered to 309 students using a patient reported experience measure between Autumn 2015 and Autumn 2021.

The results of this study indicate that people with BPD can experience recovery, whilst still experiencing symptoms, as long as they receive appropriate co-produced, recovery-orientated support and services.

Further research in this area could help shape future clinical practice by embedding a recovery-focussed programme into community services.

Literature regarding recovery has focussed on diagnoses such as schizophrenia withfew papers focussing on BPD. This is an area in need of change because a lack of research on recovery from BPD could be seen to perpetuate the view that recovery from this condition may not be possible. RCs offer co-produced and co-facilitated psychoeducational courses around recovery, enabling people to develop skills and knowledge to become experts in the self-management of their difficulties. Given the gaps within the recovery literature it is unclear how RCs can support recovery for this group of service users.

This study aims to explore the evolutionary pattern of discussion topics over time in an online depression self-help community.

Using the Latent Dirichlet Allocation (LDA) method, the authors analyzed 17,534 posts and 138,567 comments posted over 8 years on an online depression self-help group in China and identified the major discussion topics. Based on significant changes in the frequency of posts over time, the authors identified five stages of development. Through a comparative analysis of discussion topics in the five stages, the authors identified the changes in the extent and range of topics over time. The authors discuss the influence of socio-cultural factors on depressed individuals' health information behavior.

The results illustrate an evolutionary pattern of topics in users' discussion in the online depression self-help group, including five distinct stages with a sequence of topic changes. The discussion topics of the group included self-reflection, daily record, peer diagnosis, companionship support and instrumental support. While some prominent topics were discussed frequently in each stage, some topics were short-lived.

While most prior research has ignored topic changes over time, the study takes an evolutionary perspective of online discussion topics among depressed individuals. The authors provide a nuanced account of the progression of topics through five distinct stages, showing that the community experienced a sequence of changes as it developed. Identifying this evolutionary pattern extends the scope of research on depression therapy in China and offers a deeper understanding of the support that individuals with depression seek, receive and provide online.

In the state of Hawaii, it has been shown that certain ethnic minority groups, such as Filipinos and Pacific Islanders, suffer disproportionally high rates of cardiovascular disease, evidence that local health-care systems and governing bodies fail to equally extend the human right to health to all. This study aims to examine whether these ethnic health disparities in cardiovascular disease persist even within an already globally disadvantaged group, the houseless population of Hawaii.

A retrospective chart review of records from Hawaii Houseless Outreach and Medical Education Project clinic sites from 2016 to 2020 was performed to gather patient demographics and reported histories of type II diabetes, obesity, hyperlipidemia, hypertension and other cardiovascular disease diagnoses. Reported disease prevalence rates were compared between larger ethnic categories as well as ethnic subgroups.

Unexpectedly, the data revealed lower reported prevalence rates of most cardiometabolic diseases among the houseless compared to the general population. However, multiple ethnic health disparities were identified, including higher rates of diabetes and obesity among Native Hawaiians and other Pacific Islanders and higher rates of hypertension among Filipinos and Asians overall. The findings suggest that even within a generally disadvantaged houseless population, disparities in health outcomes persist between ethnic groups and that ethnocultural considerations are just as important in caring for this vulnerable population.

To the best of the authors’ knowledge, this is the first comprehensive study focusing on ethnic health disparities in cardiovascular disease and the structural processes that contribute to them, among a houseless population in the ethnically diverse state of Hawaii.

The purpose of this paper is to define the impact of training and development (T&D) in the Visegrad countries (V4) as impacted by the Covid-19 pandemic, namely, Hungary, Poland, Slovakia and the Czech Republic. These countries have some political, cultural, social and economic similarities and share some common ground in the human resource development (HRD) sectors.

The authors used the HRD theory and the human capital theory to analyse the context, operations and impact of T&D in the V4 countries due to the Covid-19 pandemic. The research was conducted in 400 companies, 100 from each of the four countries using the computer assisted web interviewing technique. The questionnaire was in a six-point Likert scale format and addressed 12 topics related to T&D: policy, expectations, procedures of diagnosis, preparation, implementation, monitoring, trainees, trainers, investment and expenditures, evaluation, results and controlling.

The authors concluded that in the Visegrad countries, Covid-19 raised expectations on T&D. This was followed by increased levels of action in diagnosis, preparation, monitoring and implementation, following pre-existing and adjusted policies. Evaluation and control were complicated. Investment and results and the human side of the T&D (trainees and trainers) were the ones for which there were more uncertainties and perplexities.

The study has the limitation of using only a small sample in four countries. For further research, the authors suggest a larger study extended to all the European Union countries, an in-depth analysis of the current data and the kurtosis on Policy of T&D.

The results of the research can be used to improve T&D programs after the Covid-19 pandemic. They could also provide information to external trainers to improve and adjust their services according to the opinions of the respondents of the study. The research findings can also serve institutions responsible for policy provision of HRD at a national level by providing possibilities to apply for funding either within national or regional funds like the National Training Fund in Poland or within European Union money at a national level.

The study is original because even if the T&D in V4 countries during the Covid-19 pandemic had already been studied separately (e.g. Mikołajczyk, 2021; Vrabcová, Urbancová 2021; Vinichenko et al., 2021), no empirical, cross-national research analysing specifically the T&D in those countries has been carried out so far. The authors use an innovative methodology, addressing 12 topics and the people involved together with the stages in which a T&D policy is divided. That makes it innovative and very relevant.