Plants to drugs: A case study of human papilloma virus and traditional chinese medicine

Various types of viral diseases are emerging as the largest menace human beings have faced in the last few decades. Since the arrival of human immunodeficiency virus, the world has seen the emergence of deadly viruses like bird flu, Ebola, Nypah, Hanta, SARS, MERS, and currently the SARS-CoV-2. Other viral diseases like herpes, human papilloma virus, and hepatitis have become so common that despite their widespread infection rates, causes of liver and cervical cancer and consequent mortalities, they have not caught the attention of the general people in a way SARS-CoV-2 has done. Unlike small pox, polio, several types of hepatitis, and, to a certain extent, HPV, most other viral diseases have proved difficult to cure with vaccines or drugs. As with many other diseases, plants can form a possible source of therapeutics for HPV. There are around 250,000 species of flowering plants in the world;each species contain a range of phytochemicals with diverse pharmacological activities. For instance, over four dozen plants have been identified with antiviral activity against herpes virus, while a number of other plants and phytochemicals have shown promise against various viruses. Promising antiviral phytochemicals include coumarins, terpenoids, flavonoids, polyphenols, and alkaloids. This chapter will attempt to summarize the present state of knowledge regarding plants, formulations, and phytochemicals (against HPV) and discuss the potential of drug discovery from the promising phytochemicals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. All rights reserved.

Emotion and Sentiment Recognition using Natural Language Processing and Machine Learning Techniques during Disaster Situation

Detecting emotions of the residents during disaster scenario is important for the government agencies to properly take care of its constituents. COVID-19 is a global disaster scenario that has caused unprecedented shutdowns, unemployment, death, and isolation. The behavioral and emotional health impact of COVID-19 is investigated in this study through the use of sentiment analysis and emotion recognition. The dataset is formed by collecting tweets from the seven months before COVID-19 became prevalent in March 2020 and the following seven months after. VADER sentiment analysis method was used to determine if a tweet was positive, negative, or neutral. For emotion recognition, several machine learning algorithms were evaluated and Convolutional Neural Network (CNN) Long-Short Term Memory (LSTM) performed better than the other models. Hence, CNN-LSTM was used to classify the emotion of each tweet as either anger, fear, joy, or sadness. Each tweet has a longitude and latitude stored with it that was geocoded to give the exact location, which was used to compare the states within the USA, and finally compare the USA as a whole with Canada, and Mexico. Sentiment analysis shows that all countries have experienced an increase in negative tweets. Emotion recognition shows that compared to Canada and Mexico, USA has experienced a steep drop in emotional health. © 2022 IEEE.

A Cross-sectional Study to Evaluate People's Knowledge, Attitude and Practice Towards Using Disinfectants and Sanitizers During COVID-19 Pandemic in Bangladesh

The devastating novel coronavirus (COVID-19) pandemic worldwide has become a global health crisis. This disease is highly contagious and caused by the transmission of severe acute respiratory syndrome, coronavirus 2 (SARS-CoV-2). To prevent the transmission of SARS-CoV-2, disinfectants and sanitizers are very effective and readily available preventive agents. In this study, knowledge, attitude and practice (KAP) levels of Bangladeshi people's were assessed regarding the use of disinfectants and sanitizers during the pandemic. An online questionnaire-based survey was conducted among the respondents from July 2021 to December 2021. A total number of 428 respondents participated in this survey. Data were analysed by the Statistical Package for the Social Sciences (SPSS) V26 software and interpreted. Results revealed that most of the respondents were knowledgeable, had a positive attitude and engaged in beneficial practice. Among the respondents, a significantly higher knowledge and practice score were observed among females (54.1% and 54.4%, respectively) than their counterpart. Moreover, people living in urban areas (71.7%) had a better attitude than the rural people (28.3%). In addition, a medium positive correlation between knowledge and attitude (r = + 0.482), a weak positive association between attitude and practice (r = +0.199), and a weak positive association between knowledge and practice (r = + 0.282) were found. Overall, majority of the respondents had better KAP scores in knowledge and attitude with relatively low scores in practice which indicates some space for betterment. Copyright © 2022, University of Dhaka. All rights reserved.

Validation of Factors Affecting Learning Experience in Emergency Remote Teaching

For more than 2 decades, online or e-learning has been the major approach to distant education. However, a new variant of online learning AKA emergency remote teaching or ERT has emerged and increasingly becoming popular. ERT refers to the temporary transition of educational activities (instruction, assessment, advising) from the traditional to online to avert the crisis. This differs from a typical online or e-learning wherein educational activities are intended to be delivered online and are thus carefully designed, planned and implemented to fulfill this intention. With regards to existing literature, few publications have identified the differences between online learning and ERT, and expressed concerns over the quality of educational activities in ERT. Currently, studies that validate student learning experience in ERT are lacking in literature. ERT is an emerging pedagogical approach that was widely adopted in Spring 2020 due to COVID-19, hence it is imperative to validate its impact in students' learning experience as well as instructors' teaching experience. In this research, we focus on the following research question: what factors affect students' learning experience and instructors' teaching experience in an emergency remote teaching? To answer this question, we collected data from 240 students and 98 instructors during the implementation of ERT in our institution in Spring and Fall of 2020. Using a combination of ANOVA and Turkey's Honestly Significantly Difference (HSD), we analyze the data to determine the factors that can be used to predict student learning experience and teaching experience in ERT. Our results of this study will inspire more studies in ERT and inform effective delivery of instructional activities in time of crisis. © 2022 IEEE.

Managing panic buying-related instabilities in supply chains: A COVID-19 pandemic perspective

Global supply chains (SCs) have been severely impacted by the COVID-19 pandemic on several levels. For example, SCs suffered from panic buying-related instabilities and multiple disruptions of supply, demand, and capacity during the pandemic. This study developed an agent-based model (ABM) to predict the effects of panic buying-related instabilities in SCs and offered strategies to improve them. The ABM model includes a simulation and optimization model of a typical SC of an essential product manufacturer (i.e., toilet paper SC) for the analysis of scenarios and strategies to observe improvements in SCs. Among the four strategies identified, the findings suggest boosting production capacity to the maximum and ensuring optimal reorder points, order sizes, and trucks helped the essential product manufacturers reduce panic buying-related instabilities in their SCs. Copyright (C) 2022 The Authors.

Evaluation of Machine Learning Approaches for Prediction of Dengue Fever

Dengue is a mosquito-borne, deadly viral disease that is a major threat to public health all over the world. Dengue and covid-19 symptoms are almost same, and sometimes, people are confused about which disease they are infected with. This year in Bangladesh dengue and covid-19 patients have been increasing at an alarming rate, and most of the time people didn’t properly recognize the disease. A developing country like Bangladesh has faced many difficulties to handle this situation. The target of this research work is to analyze the symptoms and predict the chances to get infected with dengue fever. Machine learning techniques are widely utilized in the health industry to detect fraud in treatment at lower cost, predictive analysis, cure the disease. Four machine learning algorithms are used which are support vector machine, decision tree, K-nearest neighbor, random forest to predict dengue fever based on symptoms. The results were compared for percentage split and K-fold cross-validation method for before and after applying principal component analysis. The experimental result shows that the support vector machine algorithm provides the highest performance compared to others algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Technology in medical laboratory: Improving turnaround time during pandemic

The extent of laboratory management was only related to developing, maintaining, improving and sustaining the quality of accurate test results as a way of improving clinical outcomes. This was the emphasis prior to public health emergencies such as the COVID-19 pandemic, becoming a major global challenge. In the face of such pandemic, one of the main challenges was to be able to maintain an efficient turnaround time (TAT) in the face of increased sample workload with fewer staffing per shift as a strategy to minimize overcrowding. The industrial revolution 4.0 had brought with it the change agents which are automation, internet of things (IoT) and artificial intelligence (AI), which has been timely as the recent COVID-19 pandemic emerged. Total laboratory automation, remote data access, laboratory information management systems, digital pathology are among the disruptors that have been introduced to improve workflow efficiency and ultimately TAT. This talk will focus on the history trajectory of laboratory medicine management which includes ways and means medical laboratories overcome the challenges of the impact pandemics have on TAT and how technology has shifted to improve it.

Award-winning GEO GE online platform halves poor bowel preparation rate and maintains sessile serrated adenoma detection rate in a high-performing metropolitan center

Background and Aim: Poor bowel preparation for colonoscopy leads to aborted procedures and reduced polyp and cancer detection rates, leading to increased risk for patients, inconvenience to families, and additional resource use in a burgeoning health care system. The UK's Joint Advisory Group on GI Endoscopy suggests that units have a > 90% rate of adequate preparation for successful accreditation. To improve patient education and poor preparation rates at our institution, the Project GEO - GE Online video platform was introduced in 2019. This consists of five Vimeo-hosted short educational videos to help prepare patients and their carers for their endoscopy and colonoscopy procedures, including diet and bowel preparation. We aimed to examine key performance indicators in colonoscopy, including bowel preparation, before and after the introduction of GEO. Method(s): We performed a retrospective audit in a metropolitan teaching hospital in Queensland that performs more than 6000 colonoscopies per year. A link to GEO, a set of culturally sensitive, patient-centered videos, was sent in a letter, an email, and SMS to patients preparing for endoscopy and colonoscopy. Previously, patients were required to attend the hospital and were given printed handouts for information. This audit obtained Provation MD data for a 6-month period in 2019, before the initiation of GEO, and a 6-month period after, in 2021. Incomplete colonoscopies or those without preparation reporting were excluded from the analysis. Statistics were performed with chi2 analysis, and significance was set as a P value of < 0.05. Result(s): In the 6 months of 2019, before the GEO videos, a total of 2798 colonoscopies were performed. After colonoscopies with missing data and incomplete procedures were removed, there were 2031 colonoscopies for analysis. A total of 2277 colonoscopies were included in the post-GEO dataset. Results for bowel preparation and sessile serrated adenoma (SSA) detection rate before and after GEO are shown in Table 1. Conclusion(s): Project GEO has shown a significant reduction in poor preparation rates in a high-performing center and reduced repeat procedures, while not compromising SSA detection rate. Poor preparation often leads to abandonment of procedures, waste of health resources, and significant risk and inconvenience for patients, carers, and the system provider. Project GEO has had excellent patient feedback that it is improving patient and carer education and understanding, is improving compliance, and is convenient. This has led to a massive reduction in face-to-face outpatient visits (> 10 000). GEO is also COVID-19-friendly, culturally sensitive, and reaches our patients in distant regional and rural Queensland.

Reconfigurable Strategies to Manage Uncertainties in Supply Chains Due to Large-Scale Disruptions

Global supply chains have been facing severe disruptions for the last decade. Large-scale disruptions are imposing unknown risks across the supply chain networks. These types of risks are unpredictable to assume the complexity, timing, and location of the occurrence and its simultaneously happening as businesses are challenged to operate in a volatile, uncertain, complex, and ambiguous (VUCA) environment. The COVID-19 pandemic has drastically disrupted the global supply chains, the impact of which is yet to know. Due to the time-to-time lockdown, shutdown, and border closure, global supply chains faced supplier failure, production capacity degradation, restrictions in transportations, and lack of sufficient inventory to meet the extra demand of the essential products. On the other hand, those manufacturers involved in producing luxury and low-demand products faced a huge demand fall. As a result of this, they struggled to continue their business. The long-established supply chains have been unable to manage large-scale supply chain disruptions caused by the COVID-19 pandemic. This study, thus, aimed to understand the uncertainties in supply chains in the wake of large-scale disruptions and to figure out the implications of reconfigurable strategies to manage uncertainties in supply chains due to large-scale disruption. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

THE ERA OF HIV-NEGATIVE PNEUMOCYSTIS PNEUMONIA

SESSION TITLE: Chest Infections in Immunocompromised Patients Case Posters SESSION TYPE: Case Report Posters PRESENTED ON: 10/17/2022 12:15 pm - 01:15 pm INTRODUCTION: Pneumocystis pneumonia (PCP) is a life-threatening opportunistic infection caused by Pneumocystis jirovecii. HIV-negative patients with PCP are primarily individuals receiving immunosuppressive therapy for other disease processes. In rare instances, PCP could be an initial manifestation of underlying defected or suppressed cell-mediated immunity that needs to be diagnosed to prevent morbidity and mortality. CASE PRESENTATION: 75-year-old female with a history of hypertension and hypothyroidism presented to the emergency department for evaluation of cough, fever, and shortness of breath gradually worsening over the last few weeks. She received outpatient treatment with no improvement. She was vaccinated against covid-19. On presentation, the temperature was 103F, heart rate was 108 bpm, blood pressure was 163/93 mm Hg, and oxygen saturation was 86% on room air. Hemogram showed leukocytosis with left shift with elevated inflammatory markers. Chest X-ray revealed bilateral ground glass opacities. She was started on broad-spectrum antibiotics, but symptoms worsened over the next few days. CT chest showed diffuse bilateral ground glass opacities with prominent interstitial markings. BAL obtained from bilateral upper lobes was lymphocyte predominant with pneumocystis jirovecii diagnosed on Gomori methenamine silver (GMS) staining. She was started on PCP-directed antibiotics with intravenous glucocorticoids, and workup for an underlying immunodeficiency was started. Subsequent BATS biopsy revealed diffuse organizing alveolar damage, with possible associated acute interstitial pneumonia pattern. This could be a rare manifestation of PCP or a primary presentation in the appropriate clinical setting. Autoimmune panel, leukemia, and lymphoma panel came back negative. AFB smear, HIV, EBV, CMV, HTLV I/II also returned negative. The lymphocyte subset panel revealed a CD4 count of 205 and a subsequent count a few days later of 64 with decreased total IgG. The patient was treated with high dose steroids for an extended period along with treatment for PCP however continued to decline clinically. The patient and family eventually decided to pursue comfort care. DISCUSSION: The predisposition to PCP in patients is primarily due to a decrease in cell-mediated immunity regardless of HIV infection. In our patient, the etiology of idiopathic CD4+ T cell lymphocytopenia cannot be determined due to the lack of serial laboratory data measurement. One of the proposed etiologies of ICL is systemic persistent immune activation in the setting of exogenous mRNA, the current technology that is being widely used for vaccine development. CONCLUSIONS: In this era of biotechnology, with advancements in immunosuppressive therapy and mRNA-based vaccines, increased awareness around the potential immune system activation and potential downstream complications needs to be further highlighted to raise awareness among physicians. Reference #1: Li, Y., Ghannoum, M., Deng, C., Gao, Y., Zhu, H., Yu, X., & Lavergne, V. (2017). Pneumocystis pneumonia in patients with inflammatory or autoimmune diseases: usefulness of lymphocyte subtyping. International Journal of Infectious Diseases, 57, 108-115. Reference #2: Pardi, N., Hogan, M. J., Porter, F. W., & Weissman, D. (2018). mRNA vaccines - a new era in vaccinology. Nature reviews. Drug discovery, 17(4), 261–279. https://doi.org/10.1038/nrd.2017.243 Reference #3: Vijayakumar, S., Viswanathan, S., & Aghoram, R. (2020). Idiopathic CD4 Lymphocytopenia: Current Insights. ImmunoTargets and therapy, 9, 79–93. https://doi.org/10.2147/ITT.S214139 DISCLOSURES: No relevant relationships by Santhosh Gheevarghese John No relevant relationships by Konstantin Golubykh No relevant relationships by Iuliia Kovalenko No relevant relationships by Maidah Malik No relevant relationships by Hafiz Muhammad Siddique Qurashi No relevant relationships by Taj Rahman No rel vant relationships by Tabinda Saleem

Implications of Emergency Remote Teaching During COVID-19Lockdown: an Exploratory Analysis

In Spring of 2020, universities and colleges in the USA implemented a number of alternative pedagogical measures in compliance with social distance policies to curb the spread of COVID-19 and persist in academic activities. “Emergency Remote Teaching” ERT, defined as a temporary shift of pedagogy to remote models due to crises, appears to be the most popular among these measures. The transition to ERT has a range of pedagogical implications in many areas including student engagement, technology use and access, emotional stability and student assessment. Data on students and faculty experiences with respect to these areas can offer immediate and strategic insights into the implications of ERT. Current literature focuses on the development of various pedagogical approaches and technologies for remote learning. However, in depth analysis of the implications of ERT is currently lacking. This research seeks to provide insight into the challenges and implications of ERT to pedagogy, specifically we focus on student engagement and academic performance. Hence this research seeks to answer the following research questions: (i) What are the implications of emergency remote teaching on students' learning experience? (ii) What is the impact of emergency remote teaching instructors' teaching experience? In order to answer this research question, we designed a questionnaire in “surveymonkey” and distributed this to students and faculty members at small Universities in Northern Pennsylvania. We received 240 responses. After performing an exploratory analysis on the collected data, we found that although students are engaged with course materials and university staff, peer-to-peer and student-instructor engagement are low in an ERT. Also, ERT appears to have a negative impact on assessment from both students' and instructors' perspectives. As instructors continue to search for effective and alternative pedagogical strategies to deliver their courses in the face of COVID-19, we recommend that future efforts towards implementation of ERT should focus on strategies for improving peer-to-peer and student-instructor engagement. © American Society for Engineering Education, 2022

Prediction of Type II Diabetes Using Machine Learning Approaches

Diabetes is a major threat all over the world. It is rapidly getting worse day by day. It is found that about 90% of people are affected by type 2 diabetes. Now, in this COVID-19 epidemic there is a terrible situation worldwide. In this situation, it is very risk to go hospital and check diabetes properly. In this era of technology, several machine learning techniques are utilized to evolve the software to predict diabetes more accurately so that doctors can give patients proper advice and medicine in time, which can decrease the risk of death. In this work, we tried to find an efficient model based on symptoms so that people can easily understand that they have diabetes or not and they can follow a proper food habit which can reduce the risk of health. Here, we implement four different machine learning algorithms: Decision tree, Naïve Bayes, Random Forest, and K-Nearest Neighbor. After comparing the performance by using different parameter, the experimental results showed that Naïve Bayes algorithm performed better than other algorithms. We find the highest 90.27% accuracy from Naïve Bayes algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Delivery of Digital Mental Health Therapy: Types and Effectiveness

Issues and information on mental health problems are frequently discussed through media channels. However, public awareness on mental health problems is still low. In addition, individuals with a mental health problem refuses to receive treatment. To overcome these problems, professional public health agencies around the world have designed digital mental health therapy. Digital mental health therapy has given a focus to the delivery of psychological and physical health rehabilitation treatments. Although digital mental health therapy has been shown to be effective in improving mental health conditions, however the appropriate type of digital platform used to provide the therapy for a particular type of mental health problem has yet to be studied. Therefore, this article focuses on the types of digital platforms used in therapy of mental health problems and its effectiveness on various aspects of health as well as rehabilitation. In this regard, content analysis of previous studies related to the type of digital platform and its effectiveness are done. The results of the content analysis showed that SMS the most widely used platform to deliver mental health problem therapy followed by smartphone applications, websites and online chat. Overall, the findings from the results of previous studies show positive changes in emotions, thoughts and behaviours. This suggests that mobile therapy for mental health has great potential to be implemented for individuals who have constraints to access in-person therapy or during critical times such as COVID19 pandemic situations which limiting population movement.

A Cost-Effective Solution for Real Time Remote Monitoring of Vital Signs in Patients

The outbreak of COVID-19 hit the world with an incomparable magnitude and introduced new challenges in the diagnosis and treatment of patients. Personal interactions have suddenly become dangerous which can be reduced by the use of digital technology in healthcare. Towards this, we have developed a low-cost remote vital sign monitoring system (VSM) that can be used at hospitals as well as at home for continuous and long-term monitoring of different clinical status, and provide extended support to the vulnerable patients. The proposed VSM has been designed with four layers: sensing layer, data processing layer, networking layer, and applications layer. It comes with three units: a wrist unit, a bedside monitor and a web-based graphical user interface (GUI) accessible by the nurse, physician or attendants remotely from anywhere. The effectiveness of measurement, transmission, and remote monitoring has been demonstrated by experiments. The system is designed with open source and low-cost hardware devices to ensure that it can be afforded and implemented in low resource settings of the developing countries. The proposed system can provide an effective way of delivering care to more patients while protecting everyone involved from infection.

Decision Support System of Direct Cash-Village Fund Recipients Using Multi Attribute Utility Theory

Direct Village Fund Cash Assistance (BLT-Dana Desa) is a form of assistance from the government in the form of cash to poor families in villages sourced from the Village Fund to reduce the impact of the COVID-19 pandemic. To facilitate village officials in determining aid recipients quickly, accurately and on target, the MAUT method was chosen which was deemed suitable for use in the Decision Support System (DSS) which had many criteria so that it could easily calculate each alternative based on the many types of criteria and sub-criteria used and with a predetermined weight. There are 148 data samples of BLT recipients registered in the Social Welfare Integrated Data (DTKS) of Loa Janan Ulu village. The criteria in this study are building floor Size, type of house floor, types of house walls, sanitary facilities, power source, source of drinking water, cooking fuel, consumption of chicken/meat/milk, clothing needs, consumption in a day, do not have savings max. 500.000 rupiah. Based on the results of calculations using the MAUT method, a recommendation for direct cash assistance recipients was obtained with an accuracy value of 92.57%.

A Machine Learning Approach to Track COVID-19 Pandemic using Sentiment Analysis

Coronavirus disease or COVID-19 is one of the most frightening and infectious diseases of the twenty-first century. Since the outbreak of COVID-19 in Wuhan, China, numerous researches are conducted in this sector. At the preliminary stage, there was not sufficient numeric data for research but when we consider the text data such as trending topics of Social Media or patients sharing experiences about their symptoms, we get enough data to ace the navigation of the Coronavirus (SARS-CoV-2). Keeping aside the health complications related to COVID-19, there also has been huge public panic following the pandemic. Sentiment analysis helps to learn the emotions of a vast number of people about any particular topic. In this paper, we have used sentiment analysis methods to observe the public reaction to the COVID-19 pandemic and people's experience of the ongoing vaccination process. Machine Learning-based (ML-based) classification algorithms are implemented for text classification. Finally, the accuracy of the classification models is also calculated for further prediction. © 2021 IEEE.

Utilization of Machine Learning Techniques for Prediction of COVID-19 Epidemic

COVID-19 pandemic is a deadly impact on the health and well-being of the world population. A developing country like Bangladesh has limited medical resources, and sometimes many people cannot get proper treatment in time. A continued increasing number of people tested positive for COVID-19 has caused a lot of strain on the governing bodies across the country, and they face difficulties to handle this situation. The aim of this work is to analyze the symptoms and predict the chances to get infected with COVID-19 disease. Five different machine learning algorithms are utilized to predict COVID-19 based on symptoms. Random forest, support vector machine, logistic regression, Gaussian Naive Bayes, and K-nearest neighbor algorithms have been used. We compare the performance before and after applying principal component analysis. The performance of K-nearest neighbor found the more accurate result before and after applying principal component analysis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Evaluation of Video Meeting Types for Online Teaching-Learning

Nowadays, the teaching-learning process was completely performed online due to COVID-19 existed. To support this process, a lecturer was typically used a kind of video meetings to present the teaching materials. The first type is the video-conferencing meeting in which students and lecturers can directly interact during activities. The next type is the video-recording meeting for which students view the teaching materials through a recorded video without any interactions with their lecturer. However, it is still unknown what types of video meetings are fit for online activities. Hence, this study aims to identify which video meeting types are suitable for online teaching-learning. The students (n=160) were asked to attend the online class and pay attention to the learning materials through each type of video meetings. We gained students attendance data from the lecture activities on the e-learning system. Following this, the students judged two video meeting types on how suitable they considered each type could be used for online teaching-learning. A 7-point rating scale was used to obtain the student judgments. In general, the student's attendance rate was better when the course materials were presented through the video-conferencing meeting as compared to those through the video-recording meeting. Furthermore, the video-conferencing meeting was also regarded as more appropriate to online teaching-learning than the video-recording meeting. Thus, the present results suggested that the video-conferencing meeting was a proper meeting type to present the course materials online and the lecturer can have an open dialogue in real-time.

Rural Psychology and the COVID-19 Pandemic: Highlighting Strengths and Future Directions

Public Significance Statement Rural mental health needs during the COVID-19 pandemic have not been widely discussed within the existing literature. Challenges and strengths specific to rural Canada are reviewed within the context of COVID-19 with recommendations for future directions provided to the reader. These recommendations are important for practitioners and policymakers to consider, as they provide future directions for policy development. Recommendations include ensuring mental health policies and practices implemented in rural and remote communities maintain a rural perspective. The Coronavirus disease (COVID-19) pandemic has dramatically impacted Canadians' mental health, including those who live in rural areas. Rural psychologists have long faced unique challenges associated with practice related to accessibility, isolation, and technology. They also have extensive experience in practicing with flexibility, creativity, and complex ethical considerations such as competency (generalist vs. specialist, cultural competence vs. content competence). Therefore, they may have adapted relatively rapidly to the dramatic changes that came along with the pandemic and be well positioned to lead their urban colleagues and organizations as we move forward. Whereas new and pre-existing challenges have been exacerbated by the pandemic, strengths of rural psychologists (e.g., managing geographical isolation, familiarity with telehealth) have emerged. This article looks at the strengths embedded in rural psychology that facilitated service provision during the pandemic. It also reviews future directions to build upon within the rural Canadian context.

Key performance indicators for a sustainable recovery strategy in health-care supply chains: COVID-19 pandemic perspective

Purpose: Supply chains’ (SCs’) sustainability practices and recovery strategies are attaining popularity in academia and industries to improve the resilience of the SCs and to manage large-scale disruptions. The global pandemic caused by the COVID-19 has raised the question of the sustainability of essential health-care products’ SCs of Bangladesh. It is an essential avenue for making the life of people safe and secure. Despite its importance, most of the health-care SCs in Bangladesh are struggling to meet the demand of its nation due to capacity shortage, technological backwardness of the manufacturers, delivery capacity shortages and less advanced forecasting capabilities. Therefore, this study aims to investigate the key performance indicators (KPIs) of a sustainable recovery strategy in the context of health-care SCs considering the COVID-19 pandemic. Design/methodology/approach: This study used a dynamic method named graph theory and matrix approach to evaluate the most critical KPIs of a sustainable recovery strategy in the context of Bangladeshi health-care SCs. Findings: The result revealed that dynamic forecasting and planning with a smooth delivery and distribution support system, production capacity diversification and having alternative or multiple suppliers during extraordinary disruptions may aid in the sustainability of the health-care SCs in Bangladesh. Originality/value: This study is unique as no previous study has identified and examined the sustainable recovery strategy perspective KPIs considering the COVID-19 pandemic in the context of Bangladeshi health-care SCs. This study will also add value by guiding decision-makers of the health-care SCs of Bangladesh to adopt strategies toward the sustainability of SCs. © 2021, Emerald Publishing Limited.