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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.

Current research on mental health in the construction industry is fragmented, making it difficult to obtain a complete picture of young construction workers’ mental health conditions. This situation adversely affects research progress, mental health-care planning and resource allocation. To address this challenge, the purpose of this paper was to identify the themes of mental health conditions among young construction workers and their prevalence by geographical location.

The scoping review was conducted using meta-aggregation, guided by the CoCoPop (condition [mental health], context [construction industry] and population [construction workers 35 years old and younger]) and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews) frameworks.

A total of 327 studies were retrieved, and 14 studies published between 1993 and 2022 met the inclusion criteria. The authors identified 13 mental health conditions and categorized them under nine themes. Mood disorders, anxiety disorders and substance-related disorders constituted the most researched themes. Studies predominantly focused on young male workers in the Global North. The prevalence estimates reported in most of the studies were above the respective country’s prevalence.

This review extends previous studies by focusing specifically on the themes of mental health conditions and giving attention to young construction workers whose health needs remain a global priority. The study emphasizes the need to give research attention to lesser-studied aspects of mental health, such as positive mental health. The need to focus on female construction workers and on homogenous sub-groups of young workers is also emphasized. The findings can guide future systematic reviews on the identified thematic areas and help to plan the development of interventions.

Autistic individuals are at increased risk of trauma exposure and post-traumatic stress disorder (PTSD). Diagnostic overshadowing, however, often results in PTSD symptoms being mislabelled as autistic traits. This study aims to develop professional consensus on the identification and assessment of co-occurring PTSD in autistic adults.

An online modified Delphi design was used to gather professionals’ perspectives on key aspects of the identification and assessment of PTSD in autistic adults. Data were gathered qualitatively in Round 1 and then synthesised using content analysis into a list of statements that were rated in Round 2. Statements reaching 60–79% consensus and additional suggestions were sent out for rating in Round 3. Consensus for the final statement list was set at 80% agreement.

Overall, 108 statements reached consensus. These form the basis of professional-informed recommendations to facilitate the identification and assessment of PTSD symptoms in autistic adults.

The final Delphi statements provide a framework to assist with the assessment and recognition of traumatic stress reactions in autistic adults presenting to mental health, diagnostic or social services.

To the best of the authors’ knowledge, this is the first study to explore the presentation and identification of PTSD in autistic adults (with and without intellectual disability), using a bottom-up approach informed by professional consensus.

This study aims to understand and a lay a foundation of how analytics has been used in depression management, this study conducts a systematic literature review using two techniques – text mining and manual review. The proposed methodology would aid researchers in identifying key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical research objective.

This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review.

The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure.

This study presents a new methodology for conducting a literature review for empirical research articles. This study has explored an “HMLR” that combines text mining and manual systematic literature review. Depending on the purpose of the research, these two techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and revealing areas where research might be lacking.

This study aims to compare the social support among patients with schizophrenia or methamphetamine dependency with healthy individuals.

Using convince sampling, the authors recruited 80 patients (schizophrenia, n = 40; methamphetamine dependency, n = 40) and their companions (healthy individuals, n = 40) who were referred to a psychiatric hospital in a cross-sectional study in Sari, Iran. In in-person interviews, the authors collected data on demographic characteristics and measured social support using a standardized questionnaire.

The three groups were similar regarding age and marital status, but different in gender distribution (p = 0.001). The average social support score was 58.0 in the schizophrenia group and 42.3 in the methamphetamine-dependent group, both significantly lower than 63.6 in the healthy group (p = 0.001). The social support scores in schizophrenia and methamphetamine-dependent groups were significantly lower than those in the healthy group across all subgroups of gender (p < 0.04), age (p < 0.05) and marital status (p < 0.001). The methamphetamine-dependent group had the lowest score overall and across all demographic groups and social support subdomains.

This study had two main limitations. First, the study samples were from one city and one hospital in the north of Iran and so may not be generalizable to other population and settings. Second, the authors did not study the causes or predictors of low social support like social stigma which should be studied in future studies.

Despite the limitations, this study found low social support for people diagnosed with schizophrenia or methamphetamine dependency. Intervention to increase social support for them, especially for those with substance use, is required.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

Promoting positive mental health is increasingly being encouraged as the focus of research and policies on the mental health of construction personnel. Most measures of mental health, however, typically use negative indicators such as depression and anxiety and are not specifically developed for the construction workforce, especially those with a Global South background. These limitations have made it challenging to measure construction personnel’s positive mental health. The purpose of this study was, therefore, to develop a scale for measuring the positive mental health of construction personnel with a Global South background.

Guided by Keyes’ two-continua model of mental health, the study objectives were addressed through a mixed-methods study using the case of Ghana. Qualitative data collected from eight key stakeholder groups using 16 interviews and two rounds of focus group discussions were analysed thematically. Quantitative data were obtained through a survey of 425 construction personnel and analysed using confirmatory factor analysis and correlation analysis.

Thematic analysis revealed a four-dimensional structure of positive mental health, namely, emotional, psychological, social and spiritual. Confirmatory factor analysis and correlation analysis of the results indicated good instrument validity and reliability.

Existing measures of positive mental health are based on a three-dimensional model, i.e. emotional, social and psychological well-being. By including spiritual well-being, this study proposes a four-dimensional measurement model as a more comprehensive and promising measure to use in surveys of positive mental health among the construction workforce, especially those with a Global South background, and to develop suitable interventions for them.

Deficiencies or imbalances in dietary fat intake may influence on mental and neurological functions of children with autism spectrum disorders (ASD). This study aims to compare body mass index (BMI) and the amount of fatty acids intake in the autistic patients with the comparison group.

This case-control was carried out on 200 randomly selected children from 5 to 15 years old (100 autistic patients as the case group and 100 healthy children as the comparison group) in Tehran, Iran. The food frequency questionnaire (FFQ) was used to assess the intake of calorie, macronutrients and different types of dietary fatty acids including saturated fatty acids (SFA), monounsaturated fatty acids (MUFAs), poly unsaturated fatty acids (PUFAs), linoleic acid (LA), α-Linolenic acid (ALA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and trans fatty acids.

The autistic patients had higher BMI, birth weight and mother’s BMI compared to the comparison group (All p < 0.01). No significant difference was found in the amount of dietary calorie, protein, carbohydrate and total fat intake between two groups. The risk of ASD was associated with higher intake of MUFAs (OR: 3.18, CI%:1.13–4.56, p = 0.04), PUFAs (OR: 4.12, CI95%: 2.01–6.25, p < 0.01) and LA (OR: 4.76, CI95%: 1.34–14.32, p < 0.01).

The autistic children had higher BMI and higher intake of unsaturated fatty acids except for omega-3 fatty acids. Further longitudinal studies are warranted.

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.

Recent research has proposed a specific female phenotype within autism spectrum disorder (ASD). It suggests females exhibit differences in social communication styles with higher levels of camouflaging and compensatory strategies, as well as variance in restrictive repetitive behaviours (RRBs); however, many existing studies have been based on either small, disproportionate or child and adolescent samples, leaving questions about the specific phenotype. This study aims to explore the sex difference and phenotype in a clinic sample of individuals diagnosed with autism.

A service evaluation of sex/ gender differences on 150 historical ASD assessment reports (75 males, 75 females) using a 103-item questionnaire developed from a quantitative review of existing literature was undertaken.

Females camouflaged more significantly than males in five different areas (thinking how to act next, preparing conversation in advance, making lists of prompts/social responses, wearing a mask/acting, less monotone voice); however, these were not maintained in post-analysis correction.

This study points the evidence towards a different phenotype of Autism that is more common in women than men rather than a unique female phenotype.