Mental health and well-being in children and adolescents.

The guideline discusses the various milestones in typical neurodevelopment and the various checkpoints where atypical development can be picked up. There is also a remarkable influence of epigenetics and parenting on child development and well - being. It is also essential to establish effective communication to facilitate healthy child development. Well being in children is largely impacted by schooling, curricular design, inclusivity, teacher training and awareness of newer developments, parent teacher interaction. A clinician must also be well acquainted with the National Education Program and its impact. A healthy environment, exercise, adequate nutrition, microplastics on children and adolescents, global warming are key factors in the development of children. It is indispensable for clinicians to approach well- being in a scientific way and get a clear understanding of the laws and policies for child welfare and protection.

Artificial intelligence in positive mental health: a narrative review.

The paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role in mental health. AI has a huge number of promises to offer mental health care and this paper looks at multiple facets of the same. The paper first defines AI and its scope in the area of mental health. It then looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI. The role of AI in various psychiatric disorders like neurodegenerative disorders, intellectual disability and seizures are discussed along with the role of AI in awareness, diagnosis and intervention in mental health disorders. The role of AI in positive emotional regulation and its impact in schizophrenia, autism spectrum disorders and mood disorders is also highlighted. The article also discusses the limitations of AI based approaches and the need for AI based approaches in mental health to be culturally aware, with structured flexible algorithms and an awareness of biases that can arise in AI. The ethical issues that may arise with the use of AI in mental health are also visited.

Psychometric properties and factor structure of the suicidal narrative inventory in major depression: A multicentric evaluation.

BACKGROUND: The Suicidal Narrative Inventory (SNI) is a 38-item self-report measure developed to assess elements of the suicidal narrative, a subacute, predominantly cognitive, presuicidal construct. Our objectives were to assess the factor structure, validity, and reliability of the SNI-38 among adults with major depressive disorder (MDD). METHODS: Using a cross-sectional design, we administered the Hindi version of the SNI along with other self-report measures to adults with MDD, recruited from 24 tertiary care hospitals across India. Confirmatory factor analysis (CFA) was performed to assess the factor structure of SNI-38. Reliability (internal consistency) was assessed using Cronbach's alpha (α). Convergent, discriminant, and criterion validity of the SNI-38 were tested by comparing it against other appropriate measures. RESULTS: We collected usable responses from 654 Hindi-speaking participants (Mean age = 36.9 ± 11.9 years, 50.2% female). The eight-factor solution of the SNI showed good model fit indices (χ2[637] = 3345.58, p <.001, CFI =.98, and RMSEA =.08). Internal consistencies for the SNI subscale scores were good to excellent, α ranging from .73 to.92. While most subscales significantly converged with other measures, associations were comparatively weaker and inconsistent for the 'thwarted belongingness' and 'goal reengagement' subscales. CONCLUSION: Consistent with prior data, our study confirmed an eight-factor solution and demonstrated adequate psychometric properties for the Hindi version of the SNI-38 in our sample. These findings provide empirical support for the use of SNI to assess the suicidal narrative among Indian adults with MDD.

Non-binary gender, vulnerable populations and mental health during the COVID-19 pandemic: Data from the COVID-19 MEntal health inTernational for the general population (COMET-G) study.

BACKGROUND: The COVID-19 pandemic has brought significant mental health challenges, particularly for vulnerable populations, including non-binary gender individuals. The COMET international study aimed to investigate specific risk factors for clinical depression or distress during the pandemic, also in these special populations. METHODS: Chi-square tests were used for initial screening to select only those variables which would show an initial significance. Risk Ratios (RR) were calculated, and a Multiple Backward Stepwise Linear Regression Analysis (MBSLRA) was followed with those variables given significant results at screening and with the presence of distress or depression or the lack of both of them. RESULTS: The most important risk factors for depression were female (RR = 1.59-5.49) and non-binary gender (RR = 1.56-7.41), unemployment (RR = 1.41-6.57), not working during lockdowns (RR = 1.43-5.79), bad general health (RR = 2.74-9.98), chronic somatic disorder (RR = 1.22-5.57), history of mental disorders (depression RR = 2.31-9.47; suicide attempt RR = 2.33-9.75; psychosis RR = 2.14-10.08; Bipolar disorder RR = 2.75-12.86), smoking status (RR = 1.15-5.31) and substance use (RR = 1.77-8.01). The risk factors for distress or depression that survived MBSLRA were younger age, being widowed, living alone, bad general health, being a carer, chronic somatic disorder, not working during lockdowns, being single, self-reported history of depression, bipolar disorder, self-harm, suicide attempts and of other mental disorders, smoking, alcohol, and substance use. CONCLUSIONS: Targeted preventive interventions are crucial to safeguard the mental health of vulnerable groups, emphasizing the importance of diverse samples in future research. LIMITATIONS: Online data collection may have resulted in the underrepresentation of certain population groups.