Gender-specific psychological and social impact of COVID-19 in Pakistan

Background: COVID-19 has rapidly spread across the world. Women may be especially vulnerable to depression and anxiety as a result of the pandemic.Aims: This study attempted to assess how gender affects risk perceptions, anxiety levels and behavioural responses to the COVID-19 pandemic in Pakistan, to recommend gender-responsive health policies.Methods: A cross-sectional online survey was conducted. Participants were asked to complete a sociodemographic data form, the Hospital Anxiety and Depression Scale, and questions on their risk perceptions, preventive behaviour and information exposure. Multiple logistic regression analysis was used to assess the effects of factors such as age, gender and household income on anxiety levels.Results: Of the 1391 respondents, 478 were women and 913 were men. Women considered their chances of survival to be relatively lower than men (59% v. 73%). They were also more anxious (62% v. 50%) and more likely to adopt precautionary behaviour, such as avoiding going to the hospital (78% v. 71%), not going to work (72% v. 57%) and using disinfectants (93% v. 86%). Men were more likely to trust friends, family and social media as reliable sources of COVID-19 information, whereas women were more likely to trust doctors.Conclusions: Women experience a disproportionate burden of the psychological and social impact of the pandemic compared with men. Involving doctors in healthcare communication targeting women might prove effective. Social media and radio programmes may be effective in disseminating COVID-19-related information to men

Optimizing Medical Image Classification Models for Edge Devices

Machine learning algorithms for medical diagnostics often require resource-intensive environments to run, such as expensive cloud servers or high-end GPUs, making these models impractical for use in the field. We investigate the use of model quantization and GPU-acceleration for chest X-ray classification on edge devices. We employ 3 types of quantization (dynamic range, float-16, and full int8) which we tested on models trained on the Chest-XRay14 Dataset. We achieved a 2–4x reduction in model size, offset by small decreases in the mean AUC-ROC score of 0.0%–0.9%. On ARM architectures, integer quantization was shown to improve inference latency by up to 57%. However, we also observe significant increases in latency on x86 processors. GPU acceleration also improved inference latency, but this was outweighed by kernel launch overhead. We show that optimization of diagnostic models has the potential to expand their utility to day-to-day devices used by patients and healthcare workers; however, these improvements are context- and architecture-dependent and should be tested on the relevant devices before deployment in low-resource environments

PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor

Most human activity recognition datasets that are publicly available have data captured by using either smartphones or smartwatches, which are usually placed on the waist or the wrist, respectively. These devices obtain one set of acceleration and angular velocity in the x-, y-, and z-axis from the accelerometer and the gyroscope planted in these devices. The PLHI-MC10 dataset contains data obtained by using 3 BioStamp nPoint® sensors from 7 physically healthy adult test subjects performing different exercise activities. These sensors are the state-of-the-art biomedical sensors manufactured by MC10. Each of the three sensors was attached to the subject externally on three muscles-Extensor Digitorum (Posterior Forearm), Gastrocnemius (Calf), and Pectoralis (Chest)-giving us three sets of 3 axial acceleration, two sets of 3 axial angular velocities, and 1 set of voltage values from the heart. Using three different sensors instead of a single sensor improves precision. It helps distinguish between human activities as it simultaneously captures the movement and contractions of various muscles from separate parts of the human body. Each test subject performed five activities (stairs, jogging, skipping, lifting kettlebell, basketball throws) in a supervised environment. The data is cleaned, filtered, and synced

The Progress of Heartbreak: A Short Story Collection

This short story collection aims to challenge the established concepts of womanhood and femininity by sharing the stories of female protagonists who are complicated and even unlikable at times. All of these women undergo an identity crisis that is imposed by things out of their control. By exploring topics like assimilation, racism, suicide, immigrant parents, mental illness, and childhood trauma these stories explore both the subtle and conspicuous manner in which people grow and change. The collection focuses on people who are left behind and who look back on the things they have lost like youth, love, family, passion, and opportunity. All of the female protagonists are held back by their past lives and their past selves. Thus, their identities are complicated and in flux throughout their respective narratives. Moreover, the short stories move through time in a manner that parallels the narrator’s emotional growth or stasis. As a short story collection, “The Progress of Heartbreak” illustrates the fragility and complex nature of individual identity, and the devastating impact that external and internal factors can place upon it

Natural Language Processing in Neuro-Ophthalmology

This video provides an overview of natural language processing (NLP) techniques, applications, and limitations in neuro-ophthalmology. NLP, a branch of artificial intelligence, enables computers to understand and analyze human language. This video discusses three types of NLP techniques: sentiment analysis, next word prediction, and word embeddings. We explore the impact of data on the resulting models. We also discuss limitations of NLP, including bias in word embeddings, hallucinations, and factual inaccuracies

A Brief Introduction to AI in Neuro-ophthalmology

In this video, we describe the basics of artificial intelligence and machine learning as applicable to clinical neuro-ophthalmologists. We use the example from a recent publication, where AI software was used to detect optic disc edema in fundus images

Perceived risk and distress related to COVID-19 in healthcare versus non-healthcare workers of Pakistan: a cross-sectional study

Background: Healthcare workers (HCWs) have found themselves and their families more susceptible to contracting COVID-19. This puts them at a higher risk of psychological distress, which may compromise patient care. In this study, we aim to explore the risk perceptions and psychological distress between HCWs and non-healthcare workers (NHCWs) in Pakistan. Methods: A cross-sectional study was conducted using an online self-administered questionnaire. Psychological distress was assessed through The Hospital Anxiety and Depression Scale (HADS). Comparisons were made between HCWs (front/backend, students/graduates) and NHCWs related to risk perceptions and stress levels related to COVID19. Following tests for normality (Shapiro–Wilk test), variables that fulflled the normality assumption were compared using the independent samples t-test, while for other variables Mann–Whitney U-test was employed. Pearson Chisquare test was used to compare categorical data. Multiple logistic regression techniques examined the association of participant age, gender, household income, and the presence of COVID-19 symptoms with depression and anxiety levels. Results: Data from 1406 respondents (507 HCWs and 899 NHCWs) were analyzed. No signifcant diference was observed between HCWs and NHCWs’ perception of susceptibility and severity towards COVID-19. While healthcare graduates perceived themselves (80% graduates vs 66% students, p-value 0.011) and their family (82% graduates vs 67% students, p-value 0.008) to be more susceptible to COVID-19, they were less likely to experience depression than students. Frontline HCWs involved in direct patient care perceived themselves (83% frontline vs. 70% backend, p-value 0.003) and their family (84% frontline vs. 72% backend, p-value 0.006) as more susceptible to COVID-19 than backend healthcare professionals. Over half of the respondents were anxious (54% HCWs and 55% NHCWs). Female gender, younger age, lower income, and having COVID-19 related symptoms had a signifcant efect on the anxiety levels of both HCWs and NHCWs. Conclusion: Frontline HCWs, young people, women, and individuals with lower income were at a higher risk of psychological distress due to the pandemic. Government policies should thus be directed at ensuring the menta

The effect of emotional intelligence on academic performance of medical undergraduates

Aim: To determine the association between Emotional Intelligence and academic performances of medical students at undergraduate level. Background: In our field of medical education, intelligence quotient is considered as a successful interpreter of academic performance and intelligence. However, little importance has been given to EI. Our study examined the relationship between EI and academic performance or workforce of preclinical medical students (1st and 2nd year MBBS students of Jinnah Sindh Medical University). Methods: We used a prospective, cross sectional study design and measured the EI by questionnaire based on a study by Petrides and Furnham, 2006. The grade point average (GPA) of their last semester was used to analyze the academic performance. Nonprobability consecutive technique was done. Our sample size comprises of 120 students out of which 14 were males and 106 were females of 1st and 2nd year of MBBS. The structured paper-based questionnaire also included pretest demographic information which was filled by the students, after taking a verbal consent. The data were collected in the month of July 1, 2015–July 30, 2015. Results: The number of participants was 120. The response rate was 100%. The result revealed that out of 4 factors, 2 were the most significant: well-being (P = 0.005) and sociability (P = 0.01). The value of EI was significantly higher in the male than in the female students. Conclusion: Our study proved a strong relationship between academic performance in terms of higher GPA and two individual factors of EI that were well-being and sociability. Overall EI values of male students were statistically higher and significant than female students. Thus, appropriate measures should be taken to strengthen emotional well-being in medical students for better academic performances