RESUMEN
Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.
RESUMEN
Introduction: Recent years have witnessed a persistent threat to public mental health, especially during and after the COVID-19 pandemic. Posttraumatic stress disorder (PTSD) has emerged as a pivotal concern amidst this backdrop. Concurrently, machine learning (ML) techniques have progressively applied in the realm of mental health. Therefore, our present undertaking seeks to provide a comprehensive assessment of studies employing ML methods that use diverse data modalities on the classification of people with PTSD. Methods and analysis: In pursuit of pertinent studies, we will search both English and Chinese databases from January 2000 to May 2022. Two researchers will independently conduct screening, extract data and assess study quality. We intend to employ the assessment framework introduced by Luis Francisco Ramos-Lima in 2020 for quality evaluation. Rate, standard error and 95% CIs will be utilized for effect size measurement. A Cochran's Q test will be applied to assess heterogeneity. Subgroup and sensitivity analysis will further elucidate the source of heterogeneity and funnel plots and Egger's test will detect publication bias. Ethics and dissemination: This systematic review and meta-analysis does not encompass patient interactions or engagements with healthcare providers. The outcomes of this research will be disseminated through scholarly channels, including presentations at scientific conferences and publications in peer-reviewed journals.PROSPERO registration number CRD42023342042.
RESUMEN
COVID-19 is a major public health event affecting the people worldwide. Nurses are still under immense psychological pressure. This study aimed to explore the relationship between mental fatigue and negative emotions among frontline medical staff during the COVID-19 pandemic. The study was conducted in August 2020, which included 419 medical staff between 17 to 28 years. The Fatigue Scale, Multidimensional Mental Flexibility Questionnaire, Cognitive Fusion Scale, and Depression-Anxiety-Stress Brief Version Scale were used. During the data collection period, the pandemic was under control in China and continued worldwide. The results indicated that 27.7% of the medical staff experienced depression, and 32.3% of them feel stressed. Specifically, first, correlation analyses showed significant positive pairwise correlations between mental fatigue, psychological inflexibility, cognitive fusion, and negative emotions among nurses. Second, mediation model tests showed statistically significant mediating effects of psychological inflexibility and cognitive fusion between mental fatigue on nurses' negative emotions, and statistically, significant chain mediating effects of psychological inflexibility and cognitive fusion. Mental fatigue indirectly affects nurses' negative effects through the mediating effects of psychological inflexibility, cognitive fusion, and the chain mediating effects of psychological inflexibility and cognitive fusion, respectively. the negative effects of mental fatigue come from impairment of cognitive functioning, and interventions using acceptance and commitment therapy for mental fatigue and negative emotions are more effective since both psychological inflexibility and cognitive fusion are important components of the therapy.
