Institute of Mental Health and Hospital

This qualitative study investigates the growing phenomenon of backdated research publications and their influence on academic hierarchies within Indian higher education institutions. Conducted across four major university settings—Kanpur, Lucknow, Delhi, and Varanasi—the research engages a purposive sample of 200 faculty members, with 50 teachers from each location. Using a semi-structured interview schedule, the study explores personal experiences, perceptions, and observations related to unethical publication practices, institutional pressures, and career advancement mechanisms. The responses were analyzed through thematic analysis, generating ten major themes that capture the complexity of publication-related misconduct. These themes highlight issues such as systemic loopholes, pressure to publish, compromised peer review, the role of institutional politics, and the impact on both academic integrity and meritocracy. The findings emphasize an urgent need for transparent evaluation systems, ethical oversight, and awareness-driven reforms to safeguard the credibility of academic scholarship and promotion pathways.

Background The translation and back-translation (TBT) method is widely regarded as the gold standard for translating health status measures into different languages, ensuring accuracy and cultural relevance. While machine translation (MT) can provide immediate translations, it often overlooks context and cultural nuance. A new method, machine translation with post-editing (MTPE), addresses these shortcomings by involving human translators to refine machine translations. Methods The study evaluated the TBT and MTPE methods in translating 15 self-administered (SA) scales of the Standard for Clinicians’ Interview in Psychiatry (SCIP) from English into Arabic. Results The findings demonstrated that the MTPE method was highly efficient. Through the TBT method, 4 translators required 120 hours to complete the translation of 15 SA SCIP scales. In comparison, the MTPE method required only 2 translators, who used ChatGPT for the initial translation and completed the editing in just 24 hours. The final translations produced through the MTPE method were both contextually accurate and culturally appropriate. Conclusions The integration of machine translation with human post-editing provides the combined advantages of speed, accuracy, and cultural sensitivity. The authors recommend the development of formal guidelines to support the standardized use of the MTPE method in translating health measures.

Background: Hypertension (HTN) is the primary modifiable risk factor for chronic coronary syndrome (CCS), which significantly contributes to morbidity and mortality. This study aimed to gather expert consensus from Indian cardiologists on managing HTN in patients with CCS, to optimize therapy and improve patient care. Methods: A consensus is based on Indian cardiologists’ inputs collected via an online survey. The questionnaire covered diagnostic practices, clinical patterns, and real-world treatment regimens for managing hypertensive CCS patients. Survey responses were analyzed and discussed during round table meetings. Further expert opinions were graded from Level A (strong consensus) to Level D (no consensus). Recommendations were formulated based on collective expert opinions. Results: The consensus highlights the clinical strategies for managing patients with CCS and associated comorbid conditions. The key diagnostic tools, such as ambulatory blood pressure monitoring and Holter electrocardiogram, are strongly recommended for comprehensive evaluation. Pharmacological management should prioritize the use of angiotensin II receptor blockers and beta-blockers combinations, particularly telmisartan–metoprolol fixed-dose combination (FDC) in the management of hypertensive CCS patients with type 2 diabetes mellitus or dyslipidemia. Conclusions: Indian cardiologists emphasized the importance of using, telmisartan–metoprolol FDC as a preferred drug regimen owing to their cost-effectiveness, improved efficacy, and enhanced adherence. These consensus recommendations aim to enhance therapeutic outcomes and guide future clinical practice in Indian context.

Background: Adverse childhood experiences (ACEs) are potentially traumatic events that occur in childhood. Chronic exposure to any of these experiences can lead to various physical and mental health problems in adulthood. Acquiring knowledge of the etiology of childhood adversity is very important. Hence, we aimed to study the frequency of domains of ACEs in adult offspring of patients with schizophrenia and their relationship with psychological distress. Materials and Methods: This was a cross-sectional study conducted at a tertiary mental health center for 6 months using a convenience sampling technique by standard questionnaires that include ACE – International questionnaire and Depression, Anxiety, and Stress Scale – 21 scale. Statistical analysis was done accordingly. Results: Among 66 participants aged between 18 and 30, the household treating violently (75.76%) was the most common domain affected. There were significant associations of physical abuse, emotional abuse, emotional neglect, physical neglect, bullying, household violence, and who had single or no parents with the symptoms of stress, anxiety, and depression. Conclusions: Finally, we found that >50% had ACEs in the offspring of patients with schizophrenia. The individuals who have faced ACEs can tend to recall stress, anxiety, and depression in the future.

Aims: The epidemiology of diabetic patients is well studied in India. However, there is limited data on the epidemiology of diabetes among the rural and urban communities. This study has been designed to understand the geographical pattern and presentation of diabetes in North India. Methods and Materials: An observational, multicentre, cross-sectional study was conducted among diabetic patients visiting outpatient clinics of the primary care centres at various cities of Uttar Pradesh state from January 2019 to October 2020. The patients’ epidemiological, demographic and clinical characteristics were collected with the help of a predesigned questionnaire tool. The collected data were analysed using an appropriate statistical test. Results: A total of 3,951 patient records had been studied. The median age of diabetics was 50 years, and their median BMI was 26 kg/m 2 and indicating diabetic patients were obese as per Asian Indian criteria. Patients from rural settings had 59% higher odds of not having received any formal education, and 23% higher odds of any addiction-related history. Odds of heart disease and of stroke were significantly higher in the urban setting than rural setting. Positive history of any addiction was associated with 85% higher odds of heart disease or stroke ( P < .05) in the overall adult population. Multiple regression analysis showed age of patient and liquor consumption were associated with a statistically significant association with heart disease. Conclusion: There was a discrepancy in presentation of diabetic individuals within the rural and urban communities of India.

Independent audit reports play a crucial role in assessing the financial reliability of companies. Auditors base their opinions on the accuracy and consistency of financial statements and their underlying components. This study aims to automatically predict audit opinions by leveraging financial ratios derived from financial statements, as well as well-known financial risk scores such as Altman-Z, Springate, and Zmijewski. Classification was performed using XGBoost and Random Forest algorithms. Considering data privacy requirements, the modeling process was implemented using the Concrete ML library, which supports homomorphic encryption, thereby preserving the confidentiality of financial data. A hierarchical classification approach was adopted further to subdivide unqualified audit opinions into more detailed sub-classes, enhancing interpretability. Experimental results show that the proposed model achieves strong performance in terms of both accuracy and F1 score. The developed system is expected to serve as a predictive, systematic, and privacy-aware decision support tool for auditors and other stakeholders prior to the formal audit process.

Abstract The association between Schizophrenia (SZ) and obsessive-compulsive disorder (OCD) has been known for more than a century now, also popularly known as “Schizo-obsessive disorder”. More than one-fourth of the patients with schizophrenia were found to have comorbid obsessive-compulsive disorder, posing specific diagnostic and management challenges. There are sparse indications of the effective use of electroconvulsive therapy in patients with poor response to traditional treatment modality, and here, we discuss a case of a young female who responded to electroconvulsive therapy.

BackgroundWhile effective lay-health worker models for mental health care have been demonstrated through efficacy trials, there is limited evidence of the effectiveness of these models implemented in rural LMIC settings.AimTo evaluate the impact of a volunteer community-led intervention on reduction in depression and anxiety symptoms and improvement in functioning, and social participation among people living in rural Gujarat, India.MethodsStepped-wedge cluster randomized controlled trial was used to assess the effectiveness of delivery of psychosocial intervention across 645 villages in Mehsana district of Gujarat, India between April 2017 and August 2019. The primary outcome was an improvement in depression and/or anxiety symptoms assessed using GHQ-12 at 3-month follow-up. Secondary outcomes were improvement in (a) depression and anxiety (Patient Health Questionnaire, (PHQ-9), Generalized Anxiety Disorder (GAD-7) & Self-Reporting Questionnaire-20 (SRQ-20); b) quality of life (EQ- 5D); c) functioning (WHO-DAS-12), and social participation (Social Participation Scale SPS). Generalized linear mixed-effects models were used to assess the independent effect of the intervention.ResultsOut of a total of 1191 trial participants (608- intervention & 583-control), 1014 (85%) completed 3-month follow-up. In an adjusted analysis, participants in the intervention condition showed significant recovery from symptoms of depression or anxiety (OR 2.2; 95% CI 1.2 to 4.6; p<0.05) at the end of 3-months, with effects sustained at 8-month follow-up (OR 3.0; 95% CI 1.6 to 5.9). Intervention participants had improved scores on the PHQ-9 (Adjusted mean difference (AMD) –1.8; 95%CI -3.0 to -0.6), and SRQ-20 (AMD -1.7; 95%CI -2.7 to -0.6), at 3-months and PHQ-9, GAD-7, SRQ-20, EQ-5D and WHO-DAS at 8 months follow-up.ConclusionFindings suggest that Atmiyata had a significant effect on recovery from symptoms of depression and anxiety with sustained effects at 8-month follow-up.Trial registrationTrial registration details. The trial was registered prospectively with the “Clinical Trial Registry in India” (registry number: CTRI/2017/03/008139).