Grief experience among ICU staff with loss of family members during COVID-19 outbreak in IRAN: A qualitative study

IntroductionThe COVID-19 crisis created a lot of problems in people's lives. Different lifestyles, mental health, communication, rituals and traditions, particularly those involved in mourning, have changed drastically. Medical staff faced numerous critically ill patients every day. This greatly distressed the staff, especially the ICU staff. The end result was considerable amounts of mental distress for the medical staff who lost family members to COVID-19 making the distress even more complex.MethodsWe carried out this qualitative research to study the grief experiences of 12 Iranian ICU staff members at the Rasoul Akram Hospital who had experienced the loss of a family member to the COVID-19 pandemic. We studied the effects of how their own grief experience and how constant exposure to critically ill patients influenced their work with patients. All semi-structured interviews were held in the presence of a faculty member of the psychiatry department of Iran University of Medical Sciences. The interview on the grief experience among ICU staff during the COVID-19 pandemic, consists of 4 issues: Familiarity, Experience during the COVID-19 pandemic, Grieving the loss of a family member and Effects of parallel grief.ResultsWe found five common themes in the result of the experiences of the participants based on content analysis. These consisted of: complex grieving process, new experiences for coping with loss, more empathy for patients, change the meaning of death, and the need for support in work places. Likewise, there were 22 sub themes.ConclusionPaying attention to the details of staff members' life, gender differences, and cultural aspects can give us a better understanding and perception of their grief experiences. This understanding brings out valuable points which can help policy makers pass better laws for the wellbeing of society and people in order to promote leadership in turbulent times

Prefer Parameters of Occupational Health Surveillance System (OSS) in Expert Opinions of Occupational Staffs: A Qualitative Study

Occupational Health Surveillance System (OHSS) provides a critical opportunity to monitor and evaluate occupational disorders and injuries over the time. Among more than 21 million employees in Iran, 62.08% and 38.03% had been worked in the industrial and private sections respectively. Present qualitative study was designed for collection of expert opinion of staffs in this field and determined proper characters of suitable Iranian OHSS. Present qualitative study was performed on an interview based on data from occupational medicine staffs. Some questions about OHSS definition, temporary accompaniment of occupational diseases and injuries surveillance system, OHSS promoters and consumers, type of requested data for OHSS and rewarding and controlling systems to prepare qualitative and valid OHSS data. Interview answers were read, summarized and presented. Most of study participants’ staff believed that OHSS in the scientific base must cover all of essentially its parts including disorders, hazards and accidents together. They believed that this combination was made by a team working with occupational medicine staffs and other specialties such as occupational hygienist and information technologists. They emphasized that the Iranian ministry of health had the capacity to promote OHSS and organizing executive committee with all of OHSS involved as team working in this field. Occupational staff had been focused on this fact that OHSS data must cover all of needed data of Iranian working population and their relatives. Iranian occupational registry system must be change and develop as Occupational Health Surveillance system according the main parameters which were found in occupational staff interview

Individual characteristics associated with road traffic collisions and healthcare seeking in Low- and Middle-Income Countries and territories

Incidence of road traffic collisions (RTCs), types of users involved, and healthcare requirement afterwards are essential information for efficient policy making. We analysed individual-level data from nationally representative surveys conducted in low- or middle-income countries (LMICs) between 2008-2019. We describe the weighted incidence of non-fatal RTC in the past 12 months, type of road user involved, and incidence of traffic injuries requiring medical attention. Multivariable logistic regressions were done to evaluate associated sociodemographic and economic characteristics, and alcohol use. Data were included from 90,790 individuals from 15 countries or territories. The non-fatal RTC incidence in participants aged 24-65 years was 5.2% (95% CI: 4.6-5.9), with significant differences dependent on country income status. Drivers, passengers, pedestrians and cyclists composed 37.2%, 40.3%, 11.3% and 11.2% of RTCs, respectively. The distribution of road user type varied with country income status, with divers increasing and cyclists decreasing with increasing country income status. Type of road users involved in RTCs also varied by the age and sex of the person involved, with a greater proportion of males than females involved as drivers, and a reverse pattern for pedestrians. In multivariable analysis, RTC incidence was associated with younger age, male sex, being single, and having achieved higher levels of education; there was no association with alcohol use. In a sensitivity analysis including respondents aged 18-64 years, results were similar, however, there was an association of RTC incidence with alcohol use. The incidence of injuries requiring medical attention was 1.8% (1.6-2.1). In multivariable analyses, requiring medical attention was associated with younger age, male sex, and higher wealth quintile. We found remarkable heterogeneity in RTC incidence, the type of road users involved, and the requirement for medical attention after injuries depending on country income status and socio-demographic characteristics. Targeted data-informed approaches are needed to prevent and manage RTCs

Domestic violence risk prediction in Iran using a machine learning approach by analyzing Persian textual content in social media

Domestic violence (DV) against women in Iran is a hidden societal issue. In addition to its chronic physical, mental, industrial, and economic effects on women, children, and families, DV prevents victims from receiving mental health care. On the other hand, DV campaigns on social media have encouraged victims and society to share their stories of abuse. As a result, massive amount of data has been generated about this violence, which can be used for analysis and early detection. Therefore, this study aimed to analyze and classify Persian textual content pertinent to DV against women in social media. It also aimed to use machine learning to predict the risk of this content. After collecting 53,105 tweets and captions in the Persian language from Twitter and Instagram, between April 2020 and April 2021, 1611 tweets and captions were chosen at random and categorized using criteria compiled and approved by an expert in the field of DV. Then, using machine learning algorithms, modeling and evaluation processes were performed on the tagged data. The Naïve Base model, with an accuracy of 86.77% was the most accurate model among all machine learning models for predicting critical Persian content pertinent to domestic violence on social media. The obtained findings indicate that using a machine learning approach, the risk of Persian content related to DV in social media against women can be predicted