Summary: Introduction: Healthcare professionals (HPs) have been confronted by unprecedented traumatic experiences during the novel coronavirus disease (COVID-19) pandemic, especially in countries that had not experienced similar epidemic outbreaks in recent years. Aim: To analyze the impact of the COVID-19 pandemic on the mental health of HPs. Method: We comprehensively reviewed the studies published in MEDLINE (PubMed), Web of Science and Google Scholar between December 2019 and May 2020. Results: Most studies report a high prevalence of anxiety and depressive symptoms among HPs that can be associated with: (i) COVID-19 exposure; (ii) epidemiological issues; (iii) material resources; (iv) human resources; and (v) personal factors. The role of certain variables, before, during and after the pandemic, remains unexplored. Longitudinal studies will help elucidate which factors are associated with a higher risk of developing long-lasting negative effects. Qualitative studies may contribute to understanding the influence of individual and social narratives in HPs’ distress. Conclusion: A deeper analysis on the individual, institutional, political and socio-cultural factors, meanings and values influencing HPs distress and resilience during the COVID-19 pandemic is needed.

The aim of this study was to examine the characteristics of physicians’ and nurses’ suicide attempts (SA). A retrospective review of 493 medical records of physicians and nurses admitted to an inpatient unit for health professionals; 36 patients had a recent SA. Depression, cluster B and C personality disorders, and a history of previous SA were more prevalent in patients with a recent SA compared to those without it. Both professional groups preferred drug overdose as a suicide method. Physicians made more lethal attempts and had a history of more previous stressors than nurses. Depression, cluster B and C personality disorders, and previous SA should be appropriately screened and treated in order to prevent SA amongst physicians and nurses.

Co-occurrence of mental disorders and substance use disorders (dual diagnosis) among doctors is a cause of serious concern due to its negative personal, professional, and social consequences. This work provides an overview of the prevalence of dual diagnosis among physicians, suggests a clinical etiological model to explain the development of dual diagnosis in doctors, and recommends some treatment strategies specifically for doctors. The most common presentation of dual diagnosis among doctors is the combination of alcohol use disorders and affective disorders. There are also high rates of self-medication with benzodiazepines, legal opiates, and amphetamines compared to the general population, and cannabis use disorders are increasing, mainly in young doctors. The prevalence of nicotine dependence varies from one country to another depending on the nature of public health policies. Emergency medicine physicians, psychiatrists, and anaesthesiologists are at higher risk for developing a substance use disorder compared with other doctors, perhaps because of their knowledge of and access to certain legal drugs. Two main pathways may lead doctors toward dual diagnosis: (a) the use of substances (often alcohol or self-prescribed drugs) as an unhealthy strategy to cope with their emotional or mental distress and (b) the use of substances for recreational or other purposes. In both cases, doctors tend to delay seeking help once a problem has been established, often for many years. Denial, minimization, and rationalization are common defense mechanisms, maybe because of the social stigma associated with mental or substance use disorders, the risk of losing employment/medical license, and a professional culture of perfectionism and denial of emotional needs or failures. Personal vulnerability interacts with these factors to increase the risk of a dual diagnosis developing in some individuals. When doctors with substance use disorders accept treatment in programs specifically designed for them (Physicians’ Health Programs), they show better outcomes than the general population. However, physicians with dual diagnosis have more psychological distress and worse clinical prognosis than those with substance use disorders only. Future studies should contribute to a better comprehension of the risk and protective factors and the evidence-based treatment strategies for doctors with dual diagnosis.

Background: Despite the high prevalence and significant burden of depression, underdiagnosis remains a persistent challenge. Automatic speech analysis (ASA) has emerged as a promising method for depression assessment. However, a comprehensive quantitative synthesis evaluating its diagnostic accuracy is still lacking. Objective: This systematic review and meta-analysis aimed to assess the diagnostic performance of ASA in detecting depression, considering both machine learning and deep learning approaches. Methods: We conducted a systematic search across 8 databases, including MEDLINE, PsycInfo, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar from January 2013 to April 1, 2025. We included studies published in English that evaluated the accuracy of ASA for detecting depression, and reported performance metrics such as accuracy, sensitivity, specificity, precision, or confusion matrices. Study quality was assessed using a modified version of the Quality Assessment of Studies of Diagnostic Accuracy-Revised. A 3-level meta-analysis was performed to estimate the pooled highest and lowest accuracy, sensitivity, specificity, and precision. Meta-regressions and subgroup analyses were performed to explore heterogeneity across various factors, including type of publication, artificial intelligence algorithms, speech features, speech-eliciting tasks, ground truth assessment, validation approach, dataset, dataset language, participants’ mean age, and sample size. Results: Of the 1345 records identified, 105 studies met the inclusion criteria. The pooled mean of the highest accuracy, sensitivity, specificity, and precision were 0.81 (95% CI 0.79 to 0.83), 0.84 (95% CI 0.81 to 0.86), 0.83 (95% CI 0.79 to 0.86), and 0.81 (95% CI 0.77 to 0.84), respectively, whereas the pooled mean of the lowest accuracy, sensitivity, specificity, and precision were 0.66 (95% CI 0.63 to 0.69), 0.63 (95% CI 0.58 to 0.68), 0.60 (95% CI 0.55 to 0.66), and 0.64 (95% CI 0.58 to 0.70), respectively. Conclusions: ASA shows promise as a method for detecting depression, though its readiness for clinical application as a standalone tool remains limited. At present, it should be regarded as a complementary method, with potential applications across diverse contexts. Further high-quality, peer-reviewed studies are needed to support the development of robust, generalizable models and to advance this emerging field. Trial Registration: PROSPERO CRD42023444431; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023444431