Background Moral distress is a common challenge among professional nurses when caring for their patients, especially when they need to make rapid decisions. Therefore, leaving moral distress unconsidered may jeopardize patient quality of care, safety, and satisfaction. Aim To estimate moral distress among nurses. Methods This systematic review and meta-analysis conducted systematic search in Scopus, PubMed, ProQuest, ISI Web of Knowledge, and PsycInfo up to end of February 2022. Methodological quality of included studies was assessed using the Newcastle Ottawa checklist. (...) Data from included studies were pooled by meta-analysis with random effect model in STATA software version 14. The selected key measure was mean score of moral distress total score with its’ 95% Confidence Interval was reported. Subgroup analyses and meta-regressions were conducted to identify possible sources of heterogeneity and potentially influencing variables on moral distress. Funnel plots and Begg’s Tests were used to assess publication bias. The Jackknife method was used for sensitivity analysis. Ethical consideration The protocol of this project was registered in the PROSPERO database under decree code of CRD42021267773. Results Eighty-six manuscripts with 19,537 participants from 21 countries were included. The pooled estimated mean score of moral distress was 2.55 on a 0–10 scale [95% Confidence Interval: 2.27–2.84, I2: 98.4%, Tau2:0.94]. Publication bias and small study effect was ruled out. Moral distress significantly decreased in the COVID-19 pandemic versus before. Nurses working in developing countries experienced higher level of moral distress compared to their counterparts in developed countries. Nurses' workplace (e.g., hospital ward) was not linked to severity of moral disturbance. Conclusion The results of the study showed a low level of pooled estimated score for moral distress. Although the score of moral distress was not high, nurses working in developing countries reported higher levels of moral distress than those working in developed countries. Therefore, it is necessary that future studies focus on creating a supportive environment in hospitals and medical centers for nurses to reduce moral distress and improve healthcare. (shrink)

BackgroundAcrophobia is a specific phobia characterized by a severe fear of heights. The purpose of the present study was to investigate the efficacy of two therapies that may ameliorate symptoms of acrophobia and anxiety sensitivity, i.e., virtual reality exposure therapy (VRET) and eye movement desensitization and reprocessing (EMDR) therapy with a Waiting List Control Condition (WLCC).MethodsWe applied a three-armed randomized controlled pre-post-test design with 45 female adolescent students. Students who met DSM-5 criteria for acrophobia were randomly assigned to either VRET (...) (N = 15;Mage = 17.26; SD = 1.32), EMDR (N = 15;Mage = 17.15; SD = 1.57), or a WLCC (N = 15;Mage = 17.50; SD = 1.26). The study groups were evaluated one week before the intervention and one week after the last intervention session regarding symptoms of acrophobia (Severity Measure for Acrophobia) and anxiety sensitivity (Anxiety Sensitivity Index).ResultsThe data showed that both the application of VRET and EMDR therapy were associated with significantly reduced symptoms of acrophobia (d = 1.03 for VRET and d = 1.08 for EMDR) and anxiety sensitivity (d = 1.15 for VRET and d = 1.13 for EMDR) in comparison to the Waiting List.LimitationsThe sample consisted only of adolescent women. Due to the recognizable differences between the two interventions, the therapists and the participants were not blind to the conditions.ConclusionThe results suggest that both VRET and EMDR are interventions that can significantly improve symptoms of acrophobia and anxiety sensitivity in female adolescents.Clinical Trial Registrationhttps://www.irct.ir/trial/57391, identifier: IRCT20210213050343N1. (shrink)

For decision making in government, it is necessary to have well-structured sources of information. In several countries, it is difficult to access government data as the information are dispersed, disconnected, and poorly structured. For this reason, this work presents a framework to gather, unify, and enrich missing person data from distributed web sources. The framework allows inserting new tasks specific to the user’s domain to improve data quality. In this study, Brazilian missing person data from non-governmental organizations (NGOs) and governmental (...) websites were collected and semantically enriched. To enhance the understanding of the gathered missing people cases, we create interpretive models using machine learning techniques to extract knowledge and to encourage the use of standards for publishing the data that are frequently ignored by organizations, hindering analysis and decision-making on data. After the collection and semantic enrichment process, there was an increase of approximately 11% in the data present in the base. Also, the mining process evidenced the disappearance and reappearance of a person in Brazil according to several factors such as age, state initiatives, skin tone, hair colors, etc. (shrink)