ABSTRACT
Background: Night shift duties are crucial in the ICU to ensure care continuity, where critically ill patients require round-the-clock care. However, cumulative night shift duties may disturb circadian rhythm, insomnia, fatigue, and depression, and require further elucidation. Objectives: This study aims to examine the negative consequences of various night shift patterns on insomnia, fatigue, and mental health of ICU Workers. Methods: A cross-sectional study examined how cumulative night shift duty affects insomnia, fatigue, and mental health in critical care providers (CCPs). Results: A total of 1006 participants completed this study between June 2022 and March 2023, including 54.5 % males. About 35 % were between 20 and 30 years of age, and Respiratory Therapists accounted for approximately 46.5 % of the entire sample. Most of our respondents (476; 47 %) reported working night shifts, with a monthly range of 8-15 nights. The prevalence rates for moderate to severe clinical insomnia, fatigue, and moderate to severe depression were 42 %, 48 %, and 32 %, respectively. CCPs working 8-15 nights had a 2-fold risk of clinical insomnia than those working fewer than eight nights with (AOR) and 95 % (CI) of 2.12 and 1.41-3.20, while those working ≥16 nights per month had a greater incidence of clinical insomnia compared to those working <8 nights per month, AOR (CI): 3.09 (1.90-5.03). Only those working ≥16-night shifts per month had a substantially higher fatigue risk compared to those working < 8-night shifts per month, with an AOR (CI) of 1.92 (1.19-3.08). Working 8-15-night shifts per month increases depression risks by 34 % compared to the <8-night shifts group, AOR (CI): 1.34 (0.87-2.08). Those working ≥16-night shifts per month showed a higher depression risk than those working <8-night shifts, AOR (CI): 2.53 (1.53-4.19). Conclusion: A cumulative night shift above eight nights per month is linked with an increased risk of insomnia, fatigue, and depression. The risk of these conditions was significantly directly proportional to the number of night shifts performed per month.
ABSTRACT
(1) Introduction: Around a million people are reported to die by suicide every year, and due to the stigma associated with the nature of the death, this figure is usually assumed to be an underestimate. Machine learning and artificial intelligence such as natural language processing has the potential to become a major technique for the detection, diagnosis, and treatment of people. (2) Methods: PubMed, EMBASE, MEDLINE, PsycInfo, and Global Health databases were searched for studies that reported use of NLP for suicide ideation or self-harm. (3) Result: The preliminary search of 5 databases generated 387 results. Removal of duplicates resulted in 158 potentially suitable studies. Twenty papers were finally included in this review. (4) Discussion: Studies show that combining structured and unstructured data in NLP data modelling yielded more accurate results than utilizing either alone. Additionally, to reduce suicides, people with mental problems must be continuously and passively monitored. (5) Conclusions: The use of AI&ML opens new avenues for considerably guiding risk prediction and advancing suicide prevention frameworks. The review's analysis of the included research revealed that the use of NLP may result in low-cost and effective alternatives to existing resource-intensive methods of suicide prevention.
