Mass online training of health care workers during COVID-19: approach, impact, and outcomes for over 10,000 health care providers.

OBJECTIVES: COVID-19 revealed major shortfalls in healthcare workers (HCWs) trained in acute and critical care worldwide, especially in low-resource settings. We aimed to assess mass online courses' efficacy in preparing HCWs to manage COVID-19 patients and to determine whether rapidly deployed e-learning can enhance their knowledge and confidence during a pandemic. STUDY DESIGN: Retrospective cohort study. METHODS: This international retrospective cohort study, led by a large Academic Medical Centre (AMC), was conducted via YouTube and the AMC's online learning platform. From 2020 to 2021, multidisciplinary experts developed and deployed six online training courses based on the latest evidence-based management guidelines. Participants were selected through a voluntary sample following an electronic campaign. Training outcomes were assessed using pre-and post-test questionnaires, evaluation forms, and post-training assessment surveys. Kirkpatrick's Model guided training evaluation to measure self-reported knowledge, clinical skills, and confidence improvement. We also captured the number and type of COVID-19 patients managed by HCWs after the trainings. RESULTS: Every 22.8 reach/impression and every 1.2 engagements led to a course registration. The 10,425 registrants (56.8% female, 43.1% male) represented 584 medical facilities across 154 cities. The largest segments of participants were students/interns (20.6%) and medical officers (13.4%). Of the 2169 registered participants in courses with tests, 66.9% completed post-tests. Test scores from all courses increased from the initial baseline to subsequent improvement post-course. Participants completing post-training assessment surveys reported that the online courses improved their knowledge and clinical skills (83.5%) and confidence (89.4%). Respondents managed over 19,720 COVID-19 patients after attending the courses, with 47.7% patients being moderately/severely ill. CONCLUSIONS: Participants' confidence in handling COVID-19 patients is increased by rapidly deploying mass training to a substantial target population through digital tools. The findings present a virtual education and assessment model that can be leveraged for future global public health issues, and estimates for future electronic campaigns to target.

Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach.

The unmanned aerial vehicle (UAV) industry is moving toward beyond visual line of sight (BVLOS) operations to unlock future internet of drones applications, including unmanned environmental monitoring and long-range delivery services. A reliable and ubiquitous mobile communication link plays a vital role in ensuring flight safety. Cellular networks are considered one of the main enablers of BVLOS operations. However, the existing cellular networks are designed and optimized for terrestrial use cases. To investigate the reliability of provided aerial coverage by the terrestrial cellular base stations (BSs), this article proposes six machine learning-based models to predict reference signal received power (RSRP) and reference signal received quality (RSRQ) based on the multiple linear regression, polynomial, and logarithmic methods. In this regard, first, a UAV-to-BS measurement campaign was conducted in a 4G LTE network within a suburban environment. Then, the aerial coverage was statistically analyzed and the prediction methods were developed as a function of distance and elevation angle. The results reveal the capability of terrestrial BSs in providing aerial coverage under some circumstances, which mainly depends on the distance between the UAV and BS and flight height. The performance evaluation shows that the proposed RSRP and RSRQ models achieved RMSE of 4.37 dBm and 2.71 dB for testing samples, respectively.

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms.

Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley-Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).

Workplace mistreatment and mental health in female surgeons in Pakistan.

BACKGROUND: Despite workplace mistreatment, which includes harassment, bullying and gender discrimination(GD)/bias, being serious problems for female surgeons, there are limited data from lower-middle-income countries like Pakistan. This study explored harassment and GD/bias experienced by female surgeons in Pakistan, and the effects of these experiences on mental health and well-being. METHODS: A nationwide survey was conducted between July and September 2019 in collaboration with the Association of Women Surgeons of Pakistan, an organization consisting of female surgeons and trainees in Pakistan. An anonymous online survey was emailed directly, disseminated via social media platforms (such as Facebook, Twitter and Instagram), and sent to surgical programmes in Pakistan. RESULTS: A total of 146 women surgeons responded to the survey; 67.1 per cent were trainees and the rest attending surgeons. Overall, 57.5 per cent of surgeons reported experiencing harassment, most common being verbal (64.0 per cent) and mental (45.9 per cent), but this mostly went unreported (91.5 per cent). On multivariable analysis adjusted for age and specialty, workplace harassment (odds ratio 2.02 (95 per cent c.i. 1.09 to 4.45)) and bullying (odds ratio 5.14 (95 per cent c.i. 2.00-13.17)) were significantly associated with severe self-perceived burnout, while having a support system was protective against feelings of depression (odds ratio 0.35 (95 per cent c.i. 0.16 to 0.74)). The overwhelming majority (91.3 per cent) believed that more institutional support groups were needed to help surgeons with stress reduction (78.8 per cent), receiving mentorship (74.7 per cent) and work-life balance (67.8 per cent). CONCLUSION: Workplace mistreatment, in particular harassment and bullying, has a damaging impact on the mental well-being of female surgeons, particularly trainees. The absence of support groups in Pakistan should be urgently addressed so that surgeons, especially trainees, may cope better with potentially harmful workplace stressors.

Outcome of selective non-operative management of penetrating abdominal injuries from the North American National Trauma Database.

BACKGROUND: The aim of this study was to investigate trends in the practice of selective non-operative management (SNOM) for penetrating abdominal injury (PAI) and to determine factors associated with its failure. METHODS: The National Trauma Data Bank for 2002-2008 was reviewed. Patients with PAI were categorized as those who underwent successful SNOM (operative management not required) and those who failed SNOM (surgery required more than 4 h after admission). Yearly rates of SNOM versus non-therapeutic laparotomy (NTL) were plotted. Multivariable regression analysis was performed to identify factors associated with failed SNOM and mortality. RESULTS: A total of 12 707 patients with abdominal gunshot and 13 030 with stab wounds were identified. Rates of SNOM were 22.2 per cent for gunshot and 33.9 per cent for stab wounds, and increased with time (P < 0.001). There was a strong correlation between the rise in SNOM and the decline in NTL (r = - 0.70). SNOM failed in 20.8 and 15.2 per cent of patients with gunshot and stab wounds respectively. Factors predicting failure included the need for blood transfusion (odds ratio (OR) 1.96, 95 per cent confidence interval 1.11 to 3.46) and a higher injury score. Failed SNOM was independently associated with mortality in both the gunshot (OR 4.48, 2.07 to 9.70) and stab (OR 9.83, 3.44 to 28.00) wound groups. CONCLUSION: The practice of SNOM is increasing, with an associated decrease in the rate of NTL for PAI. In most instances SNOM is successful; however, its failure is associated with increased mortality. Careful patient selection and adherence to protocols designed to decrease the failure rate of SNOM are recommended.

Validating the Injury Severity Score (ISS) in different populations: ISS predicts mortality better among Hispanics and females.

INTRODUCTION: The Injury Severity Score (ISS) is the most commonly used measure of injury severity. The score has been shown to have excellent predictive capability for trauma mortality and has been validated in multiple data sets. However, the score has never been tested to see if its discriminatory ability is affected by differences in race and gender. OBJECTIVE: This study is aimed at validating the ISS in men and women and in three different race/ethnic groups using a nationwide database. METHODS: Retrospective analysis of patients age 18-64 y in the National Trauma Data Bank 7.0 with blunt trauma was performed. ISS was categorized as mild (<9,) moderate (9-15), severe (16-25), and profound (>25). Logistic regression was done to measure the relative odds of mortality associated with a change in ISS categories. The discriminatory ability was compared using the receiver operating characteristics curves (ROC). A P value testing the equality of the ROC curves was calculated. Age stratified analyses were also conducted. RESULTS: A total of 872,102 patients had complete data for the analysis on ethnicity, while 763,549 patients were included in the gender analysis. The overall mortality rate was 3.7%. ROC in Whites was 0.8617, in Blacks 0.8586, and in Hispanics 0.8869. Hispanics have a statistically significant higher ROC (P value < 0.001). Similar results were observed within each age category. ROC curves were also significantly higher in females than in males. CONCLUSION: The ISS possesses excellent discriminatory ability in all populations as indicated by the high ROCs.