Aims: This review explores the quality of life of adult congenital heart disease patients and the relationship between disease severity and quality of life.Methods: We searched seven electronic databases and the bibliography of articles. The 31 selected studies fulfilled the following criteria: adult population; quantitative; assessment of quality of life and/or impact of disease severity on quality of life using validated measures; English language. Data extraction forms were used to summarise the results.Results: There are evident methodological limitations within the reviewed studies such as heterogeneous populations, designs, and quality of life conceptualisations and measurements. Despite these problems, findings suggest that the quality of life of adult congenital heart disease patients is compromised in the physical domain compared with their healthy counterparts, whereas no differences were found in relation to the psychosocial and environmental/occupational domain. Some severity variables appear to be significant correlates of quality of life and could be considered in a future standardised classification of disease severity.Conclusion: The methodological limitations of past research in relation to the definition and measurement of quality of life, the study designs, and disease severity classifications need to be addressed in future studies in order to provide robust evidence and valid conclusions in this area of study. This will enable the development of targeted interventions for the improvement of quality of life in the adult population of congenital heart disease patients.

Today, with the advent of Large-scale generative Language Models (LLMs) it is now possible to simulate free responses to interview questions such as those traditionally analyzed using qualitative research methods. Qualitative methodology encompasses a broad family of techniques involving manual analysis of open-ended interviews or conversations conducted freely in natural language. Here we consider whether artificial “silicon participants” generated by LLMs may be productively studied using qualitative analysis methods in such a way as to generate insights that could generalize to real human populations. The key concept in our analysis is algorithmic fidelity, a validity concept capturing the degree to which LLM-generated outputs mirror human sub-populations’ beliefs and attitudes. By definition, high algorithmic fidelity suggests that latent beliefs elicited from LLMs may generalize to real humans, whereas low algorithmic fidelity renders such research invalid. Here we used an LLM to generate interviews with “silicon participants” matching specific demographic characteristics one-for-one with a set of human participants. Using framework-based qualitative analysis, we showed the key themes obtained from both human and silicon participants were strikingly similar. However, when we analyzed the structure and tone of the interviews we found even more striking differences. We also found evidence of a hyper-accuracy distortion. We conclude that the LLM we tested (GPT-3.5) does not have sufficient algorithmic fidelity to expect in silico research on it to generalize to real human populations. However, rapid advances in artificial intelligence raise the possibility that algorithmic fidelity may improve in the future. Thus we stress the need to establish epistemic norms now around how to assess the validity of LLM-based qualitative research, especially concerning the need to ensure the representation of heterogeneous lived experiences.

Aims and objectives: This study aimed to examine the psychosocial impact and identify risk factors for poor psychosocial outcomes in healthcare professionals during the Coronavirus disease 2019 (COVID‐19) pandemic in Cyprus. Background: Healthcare professionals are in the forefront of the COVID‐19 pandemic facing an unprecedented global health crisis, which can have consequences on their psychosocial health. There is a need to identify risk factors for poor psychosocial outcomes to inform the design of tailored psychological interventions. Design: Cross‐sectional online study. Methods: A total of 1071 healthcare professionals completed self‐report questionnaires. Measures included sociodemographic information, COVID‐19‐related characteristics, quality of life (Brief World Health Organization Quality of Life; WHOQOL‐Bref), anxiety (Generalized Anxiety Disorder‐7; GAD‐7), depression (Patient Health Questionnaire‐8; PHQ‐8), occupational burnout (Copenhagen Burnout Inventory; CBI), and coping (Brief Coping Orientation to Problems Experienced; Brief COPE). This article follows the STROBE reporting guidelines. Results: The prevalence of moderate to severe anxiety and clinically significant depression was 27.6% and 26.8%, respectively. Significant risk factors for poor psychological outcomes included being female, being a nurse or doctor (vs non‐medical professional), working in frontline units (inpatient, intensive care), perceptions of inadequate workplace preparation to deal with the pandemic, and using avoidance coping. Depression and occupational burnout were significant risk factors for poor quality of life. Conclusion: The findings suggest several individual, psychosocial, and organisational risk factors for the adverse psychological outcomes observed in healthcare professionals during the COVID‐19 pandemic. Relevance to clinical practice: This study highlights the urgent need for screening for anxiety and depression and psychological interventions to combat an imminent mental health crisis in healthcare professionals during the COVID‐19 pandemic. Pandemic response protocols and public health initiatives aiming to improve and prevent mental health problems in healthcare professionals during the current and future health crises, need to account for the various factors at play.

Background: There is variability in the impact of adult congenital heart disease (ACHD) on health-related quality of life (HRQoL). A greater insight into the impact of ACHD may be gained from investigating HRQoL in various diagnostic groups and considering the importance of psychosocial risk factors for poor HRQoL. Objective: We compared the HRQoL of people with ACHD with normative data from the general population and among 4 diagnostic groups and identified risk factors for poor HRQoL in ACHD from a comprehensive set of sociodemographic, clinical, and psychosocial factors. Methods: We conducted a cross-sectional study with 303 participants from 4 diagnostic groups Simple, Tetralogy of Fallot, Transposition of the Great Arteries, Single Ventricle who completed measures of illness perceptions, coping, social support, mood, and generic and disease-specific HRQoL. Data were analyzed using 1-sample t tests, analysis of variance, and hierarchical multiple regressions. Results: There was diminished psychosocial HRQoL in the Simple group compared with the general population. Consistently significant risk factors for poor HRQoL included younger age, a perception of more severe symptoms due to ACHD, depression, and anxiety. Clinical factors were poor predictors of HRQoL. Conclusions: The findings highlight the need to develop intervention studies aiming to improve HRQoL in people with ACHD and the routine assessment of illness perceptions and mood problems during key periods in people's lives. This will help address patient misconceptions that could be tackled by clinicians or specialist nurses during routine outpatient appointments and identify people in need of psychological support.