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Diagnostics
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Advances in Mental Health Diagnosis and Screening
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Advances in Mental Health Diagnosis and Screening

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 12315

Special Issue Editor

Dr. Elaine Chapman

E-Mail Website
Guest Editor
Graduate School of Education, The University of Western Australia, Crawley 6009, Australia
Interests: psychology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue highlights the latest advancements in mental health diagnosis and screening. It highlights innovative techniques and technologies that improve accuracy, efficiency, and the early detection of mental illnesses. With contributions from leading experts, this issue provides a comprehensive overview of the latest developments and offers a roadmap for future research in this critical area.

Dr. Elaine Chapman
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mental health
  • diagnosis
  • screening
  • marker
  • prognosis

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Related Special Issue

Published Papers (6 papers)

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Research

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15 pages, 1605 KiB  
Article
Validity of the Strengths and Difficulties Questionnaire for Screening and Diagnosis in Western Australian Adolescents
by Carolyn Maxwell, Elaine Chapman and Stephen Houghton
Diagnostics 2024, 14(21), 2433; https://doi.org/10.3390/diagnostics14212433 - 30 Oct 2024
Viewed by 1093
Abstract
Background/Objectives: The Strengths and Difficulties Questionnaire (SDQ) is a widely used 25-item screening and diagnostic tool for behavioral and emotional problems in young people. Despite its popularity, evaluations of the SDQ’s factor structure in adolescent populations have produced disparate results, and its relationships [...] Read more.
Background/Objectives: The Strengths and Difficulties Questionnaire (SDQ) is a widely used 25-item screening and diagnostic tool for behavioral and emotional problems in young people. Despite its popularity, evaluations of the SDQ’s factor structure in adolescent populations have produced disparate results, and its relationships with theoretically related variables are rarely evaluated. In the present study, these two elements of validity were evaluated based on a large sample of Western Australian adolescents. Methods: Participants were 1489 adolescents, n = 623 males with a mean age of 13.79 years (SD = 1.61) and n = 866 females, with a mean age of 14.29 years (SD = 1.51). Participants completed the SDQ alongside measures of loneliness, sense of belonging, depression, bullying, and diagnostic status to evaluate its internal structure and correlations with theoretically related variables. Results: Confirmatory factor analyses supported the internal structure of the SDQ both for males and for females. Relationships between the SDQ subscale scores and those from theoretically related variables were also aligned with the instrument’s underpinning framework. Conclusions: Despite the somewhat disparate results of previous studies, overall, this study supported the validity of the SDQ for use in the Western Australian context. Full article
(This article belongs to the Special Issue Advances in Mental Health Diagnosis and Screening)
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12 pages, 825 KiB  
Article
Development of a Short-Form Hwa-Byung Symptom Scale Using Machine Learning Approaches
by Chan-Young Kwon, Boram Lee, Sung-Hee Kim, Seok Chan Jeong and Jong-Woo Kim
Diagnostics 2024, 14(21), 2419; https://doi.org/10.3390/diagnostics14212419 - 30 Oct 2024
Viewed by 973
Abstract
Background/Objectives: Hwa-byung (HB), also known as “anger syndrome” or “fire illness”, is a culture-bound syndrome primarily observed among Koreans. This study aims to develop a short-form version of the HB symptom scale using machine learning approaches. Methods: Utilizing exploratory factor analysis (EFA) [...] Read more.
Background/Objectives: Hwa-byung (HB), also known as “anger syndrome” or “fire illness”, is a culture-bound syndrome primarily observed among Koreans. This study aims to develop a short-form version of the HB symptom scale using machine learning approaches. Methods: Utilizing exploratory factor analysis (EFA) and various machine learning techniques (i.e., XGBoost, Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, and Multi-Layer Perceptron), we sought to create an efficient HB assessment tool. A survey was conducted on 500 Korean adults using the original 15-item HB symptom scale. Results: The EFA revealed two distinct factors: psychological symptoms and somatic manifestations of HB. Statistical testing showed no significant differences between using different numbers of items per factor (ANOVA: F = 0.8593, p = 0.5051), supporting a minimalist approach with one item per factor. The resulting two-item short-form scale (Q3 and Q10) demonstrated high predictive power for the presence of HB. Multiple machine learning models achieved a consistent accuracy (90.00% for most models) with high discriminative ability (AUC = 0.9436–0.9579), with the Multi-Layer Perceptron showing the highest performance (AUC = 0.9579). The models showed balanced performance in identifying both HB and non-HB cases, with precision and recall values consistently around 0.90. Conclusions: The findings of this study highlighted the effectiveness of integrating EFA and artificial intelligence via machine learning in developing practical assessment tools. This study contributes to advancing methodological approaches for scale development and offers a model for creating efficient assessments of Korean medicine. Full article
(This article belongs to the Special Issue Advances in Mental Health Diagnosis and Screening)
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14 pages, 305 KiB  
Article
The Prevalence of Autistic Traits in a Sample of Young Adults Referred to a Generalized Mental Health Outpatient Clinic
by Irene Folatti, Giulia Santangelo, Claudio Sanguineti, Sanem Inci, Raffaella Faggioli, Angelo Bertani, Veronica Nisticò and Benedetta Demartini
Diagnostics 2024, 14(21), 2418; https://doi.org/10.3390/diagnostics14212418 - 30 Oct 2024
Viewed by 1112
Abstract
Background/Objectives: The diagnosis of Autism Spectrum Disorders (ASD) is undergoing significant revisions, impacting prevalence estimates in the general population. Moreover, the rise of a dimensional perspective on psychopathology has broadened our understanding of autism, recognizing that subthreshold autistic features extend throughout the general [...] Read more.
Background/Objectives: The diagnosis of Autism Spectrum Disorders (ASD) is undergoing significant revisions, impacting prevalence estimates in the general population. Moreover, the rise of a dimensional perspective on psychopathology has broadened our understanding of autism, recognizing that subthreshold autistic features extend throughout the general population. However, there remains a limited understanding of the prevalence of ASD traits in individuals with psychiatric disorders, particularly in young adults, who are at an age where several mental health conditions emerge. The aim of this study was to evaluate the prevalence of ASD traits in a sample of young adults (18–24 years old) attending a generalized mental health outpatient clinic. Methods: A total of 259 young adult patients completed the self-report screening questionnaires Autism Quotient (AQ) and Ritvo Autism and Asperger Diagnostic Scale-Revised (RAADS-R). Results: A total of 16.2% of our sample scored above the cut-off in both scales; this percentage decreased to 13.13% when restricting the RAADS-R cut-off to >119, as suggested for clinical samples. The association with sociodemographic features is discussed. Conclusions: We argue that screening for autistic traits should be integrated into the assessment of young adults presenting with nonspecific psychiatric symptoms or psychological distress. Although there is ongoing debate over the use of self-report screening tools, a positive result on both the AQ and RAADS-R should prompt clinicians to pursue a comprehensive diagnostic evaluation using structured or semi-structured interviews. Full article
(This article belongs to the Special Issue Advances in Mental Health Diagnosis and Screening)

Review

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16 pages, 1181 KiB  
Review
A Narrative Review of Burnout Syndrome in Medical Personnel
by Andreea-Petra Ungur, Maria Bârsan, Andreea-Iulia Socaciu, Armand Gabriel Râjnoveanu, Răzvan Ionuț, Letiția Goia and Lucia Maria Procopciuc
Diagnostics 2024, 14(17), 1971; https://doi.org/10.3390/diagnostics14171971 - 6 Sep 2024
Cited by 4 | Viewed by 5031
Abstract
Burnout among healthcare workers has been extensively studied since its initial recognition in 1960, with its defining characteristics established by Maslach in 1982. The syndrome, characterized by emotional exhaustion, depersonalization, and low personal accomplishment, is exacerbated by work-related stress and has profound implications [...] Read more.
Burnout among healthcare workers has been extensively studied since its initial recognition in 1960, with its defining characteristics established by Maslach in 1982. The syndrome, characterized by emotional exhaustion, depersonalization, and low personal accomplishment, is exacerbated by work-related stress and has profound implications for individual and societal well-being. Methods: A review of the literature, including PubMed searches and analyses of risk factors and protective measures, was conducted to assess the prevalence, impacts, and biomarkers associated with burnout among healthcare workers. Various instruments for evaluating burnout were examined, including the widely used Maslach Burnout Inventory, alongside specific tools tailored to different occupational populations. Results: Healthcare workers, particularly physicians, exhibit significantly higher rates of burnout compared to the general population. Factors such as night shifts, workload, and exposure to biohazards contribute to elevated burnout risk. Biomarkers like cortisol, melatonin, and thyroid hormones have been linked to burnout, highlighting physiological implications. Conclusions: Burnout poses significant challenges to healthcare systems globally, impacting patient care, worker retention, and overall well-being. Identifying and addressing risk factors while promoting protective factors such as resilience and social support are crucial in mitigating burnout. Further research into prevention strategies and biomarker monitoring is warranted to support the mental and physical health of healthcare workers. Full article
(This article belongs to the Special Issue Advances in Mental Health Diagnosis and Screening)
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21 pages, 500 KiB  
Review
Revealing the Causes of Dyslexia through a Differential Diagnosis, a Short-Term Effective Treatment and an Appropriate Conceptual Framework
by Reinhard Werth
Diagnostics 2024, 14(17), 1965; https://doi.org/10.3390/diagnostics14171965 - 6 Sep 2024
Viewed by 1816
Abstract
Various different impairments and their interactions can cause reading problems referred to as “dyslexia”. Since reading requires the interaction of many abilities, the impairment of each of these abilities can result in dyslexia. Therefore, the diagnosis must differentiate various kinds of dyslexia. The [...] Read more.
Various different impairments and their interactions can cause reading problems referred to as “dyslexia”. Since reading requires the interaction of many abilities, the impairment of each of these abilities can result in dyslexia. Therefore, the diagnosis must differentiate various kinds of dyslexia. The diagnosis of a certain kind of dyslexia cannot be delimited to the investigation and description of symptoms but must also include the investigation of the causes of each kind of dyslexia. For this purpose, a scientifically unequivocal concept of causation and appropriate methods are needed to distinguish them from co-existing impairments that have no causal influence on reading performance. The results of applying these methods cannot be adequately accounted for by a non-scientific, intuitive understanding of necessary and sufficient conditions and causation. The methods suitable for revealing the causes of dyslexia are described in detail, and the results of applying these methods in experiments, in which 356 children with developmental dyslexia participated, are reviewed. Since the concepts of “necessary” and “sufficient” conditions and “causation” proposed in the philosophy of science are not suitable for describing causes of dyslexia and their interaction, they are replaced by a more detailed, experimentally based conceptual framework that provides an accurate description of the conditions required for correct reading and the causes of dyslexia. Full article
(This article belongs to the Special Issue Advances in Mental Health Diagnosis and Screening)
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43 pages, 354 KiB  
Review
Beyond Diagnosis and Comorbidities—A Scoping Review of the Best Tools to Measure Complexity for Populations with Mental Illness
by Grace Kapustianyk, Anna Durbin, Ali Shukor and Samuel Law
Diagnostics 2024, 14(12), 1300; https://doi.org/10.3390/diagnostics14121300 - 19 Jun 2024
Viewed by 1501
Abstract
Beyond the challenges of diagnosis, complexity measurement in clients with mental illness is an important but under-recognized area. Accurate and appropriate psychiatric diagnoses are essential, and further complexity measurements could contribute to improving patient understanding, referral, and service matching and coordination, outcome evaluation, [...] Read more.
Beyond the challenges of diagnosis, complexity measurement in clients with mental illness is an important but under-recognized area. Accurate and appropriate psychiatric diagnoses are essential, and further complexity measurements could contribute to improving patient understanding, referral, and service matching and coordination, outcome evaluation, and system-level care planning. Myriad conceptualizations, frameworks, and definitions of patient complexity exist, which are operationalized by a variety of complexity measuring tools. A limited number of these tools are developed for people with mental illness, and they differ in the extent to which they capture clinical, psychosocial, economic, and environmental domains. Guided by the PRISMA Extension for Scoping Reviews, this review evaluates the tools best suited for different mental health settings. The search found 5345 articles published until November 2023 and screened 14 qualified papers and corresponding tools. For each of these, detailed data on their use of psychiatric diagnostic categories, definition of complexity, primary aim and purpose, context of use and settings for their validation, best target populations, historical references, extent of biopsychosocial information inclusion, database and input technology required, and performance assessments were extracted, analyzed, and presented for comparisons. Two tools—the INTERMED, a clinician-scored and multiple healthcare data-sourced tool, and the VCAT, a computer-based instrument that utilizes healthcare databases to generate a comprehensive picture of complexity—are exemplary among the tools reviewed. Information on these limited but suitable tools related to their unique characteristics and utilities, and specialized recommendations for their use in mental health settings could contribute to improved patient care. Full article
(This article belongs to the Special Issue Advances in Mental Health Diagnosis and Screening)
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