Saliency Score Research Articles

Body image appearance in Parkinson's disease patients on device aided therapies.

Parkinson's disease patients may experience altered body image perception. Advanced Parkinson's disease (aPD) with motor complications often requires surgical and device-aided treatments (DAT), such as levodopa-carbidopa intestinal gel (LCIG) and deep brain stimulation (DBS). Understanding body image perception is crucial when managing these devices. This study aims to explore body image perception in aPD patients, hypothesizing a link between DAT and body image perception. We performed a cross-sectional study including non-demented aPD patients with and without DAT and age- and sex-matched controls. Participants were assessed using the Appearance Schemas Inventory-Revised (ASI-R), including Motivational Salience (MS) and Self-Evaluative Salience (SES) scores. Additional data included age, education, BMI, comorbidities, pharmacotherapy, and psychopathologies. PD patients were also evaluated with UPDRS, Hoehn and Yahr scales and LEDD calculation. 70 aPD and 36 controls were enrolled. No differences in ASI-R scores were found between PD patients and controls, but women with PD had significantly lower MS scores than controls (16.1 ± 5.6 vs 19.7 ± 5.8; p = 0.023). Among aPD patients, those on DAT had longer disease duration, higher Hoehn and Yahr, and lower UPDRS IV scores. The lowest MS was observed in women on LCIG (12.7 ± 3.3; p = 0.001). This study shows low MS ratings driven by female gender and LCIG treatment. Women on LCIG show reduced attention and management of their appearance. This may be influenced by cultural, environmental, and biological factors. Prospective research is needed to understand the impact of DAT on body image.

Pro-environmental voting: What it is, how to measure it, and its determinants among contemporary European voters

Abstract With this research, I introduce the concept of pro-environmental voting, proposing an outcome-oriented rather than motivation-based definition and measurement. By conceptualizing it as an instance of pro-environmental behavior, I define it as voting for a party that takes a pro-environmental stance and for which environmental protection is also relatively salient—regardless of whether that is why the voter supports that party. I suggest measuring it by multiplying, for the party voted, its salience score on environmental protection with the distance between its position on the environment and the average position of all parties in the system on the same issue. Then, I apply this measurement to the recently released ESS10 (2023) data from 26 European countries. Findings primarily reveal that moral obligation and age are key drivers of pro-environmental voting; they also show that younger individuals vote more consistently with their environmental concern than older voters.

Growing violence against medical professionals: A qualitative analysis of a worrying epidemic

Introduction. Increasing numbers of violent incidents against doctors have made headlines on a regular basis during the last few decades. Research suggests that doctors are disproportionately vulnerable to verbal abuse, threats, physical violence, and cyberbullying at work. As per the Indian Medical Association, verbal or physical abuse is experienced by 75% of doctors, and 43% of them consider it to be a significant cause of concern. Poor communication, overworked and understaffed medical staff, a lack of emergency care and management training, and inefficient grievance redressal procedures are probably contributing reasons. India responded by passing the Health Services Personnel and Clinical Establishments Bill, which is a noteworthy first step in resolving this matter. We must investigate different ways to promote peace, apply systemic adjustments in the provision of healthcare, and avert such unfavorable events in order to guarantee ongoing, excellent services for those who require them. The study set out to determine the likely reasons behind the rise in violence against physicians and to look into ways to make the situation better. Setting and design. Kasturba Medical College in Mangalore served as the study's location. A descriptive qualitative study with two steps is this one. Sorting of piles and free listing. Materials and methodology. Purposive sampling was employed in this investigation. Two interns and two tutors who were employed by Kasturba Medical College in Mangalore's community medicine department participated in one-on-one interviews for the study. The statistical analysis was conducted using Visual Anthropac 4.98.1 software. Results. A total of 28 causes and 19 solutions were found after free listing. A limit of 0.15 for Smith's salience score was set, and after that, 14 causes and 12 solutions were classified into groups of 3-6 and 3-5, respectively. A stronger triage system for emergency situations was the remedy with the highest salience value of 0.25, while undervaluing autonomy was the cause with the highest score of 0.25. Conclusions. In conclusion, one of the main issues we are currently facing is violence against doctors. Favoritism and discrimination among patients were identified as some of the main causes of violence against doctors by using the free listing and pile sorting method, which does not prioritize autonomy. Stricter laws against violence against doctors and an improved triage system for emergency cases were suggested as effective solutions for the aforementioned issue.

KepSalinst: Using Peripheral Points to Delineate Salient Instances.

Salient instance segmentation (SIS) is an emerging field that evolves from salient object detection (SOD), aiming at identifying individual salient instances using segmentation maps. Inspired by the success of dynamic convolutions in segmentation tasks, this article introduces a keypoints-based SIS network (KepSalinst). It employs multiple keypoints, that is, the center and several peripheral points of an instance, as effective geometrical guidance for dynamic convolutions. The features at peripheral points can help roughly delineate the spatial extent of the instance and complement the information inside the central features. To fully exploit the complementary components within these features, we design a differentiated patterns fusion (DPF) module. This ensures that the resulting dynamic convolutional filters formed by these features are sufficiently comprehensive for precise segmentation. Furthermore, we introduce a high-level semantic guided saliency (HSGS) module. This module enhances the perception of saliency by predicting a map for the input image to estimate a saliency score for each segmented instance. On four SIS datasets (ILSO, SOC, SIS10K, and COME15K), our KepSalinst outperforms all previous models qualitatively and quantitatively.

Binocularly suppressed stimuli induce brain activities related to aesthetic emotions

IntroductionAesthetic emotions are a class of emotions aroused by evaluating aesthetically appealing objects or events. While evolutionary aesthetics suggests the adaptive roles of these emotions, empirical assessments are lacking. Previous neuroscientific studies have demonstrated that visual stimuli carrying evolutionarily important information induce neural responses even when presented non-consciously. To examine the evolutionary importance of aesthetic emotions, we conducted a neuroscientific study using magnetoencephalography (MEG) to measure induced neural responses to non-consciously presented portrait paintings categorised as biological and non-biological and examined associations between the induced responses and aesthetic ratings.MethodsMEG and pre-rating data were collected from 23 participants. The pre-rating included visual analogue scales for object saliency, facial saliency, liking, and beauty scores, in addition to ‘biologi-ness,’ which was used for subcategorising stimuli into biological and non-biological. The stimuli were presented non-consciously using a continuous flash suppression paradigm or consciously using binocular presentation without flashing masks, while dichotomic behavioural responses were obtained (beauty or non-beauty). Time-frequency decomposed MEG data were used for correlation analysis with pre-rating scores for each category.ResultsBehavioural data revealed that saliency scores of non-consciously presented stimuli influenced dichotomic responses (beauty or non-beauty). MEG data showed that non-consciously presented portrait paintings induced spatiotemporally distributed low-frequency brain activities associated with aesthetic ratings, which were distinct between the biological and non-biological categories and conscious and non-conscious conditions.ConclusionAesthetic emotion holds evolutionary significance for humans. Neural pathways are sensitive to visual images that arouse aesthetic emotion in distinct ways for biological and non-biological categories, which are further influenced by consciousness. These differences likely reflect the diversity in mechanisms of aesthetic processing, such as processing fluency, active elaboration, and predictive processing. The aesthetic processing of non-conscious stimuli appears to be characterised by fluency-driven affective processing, while top-down regulatory processes are suppressed. This study provides the first empirical evidence supporting the evolutionary significance of aesthetic processing.

Overshadowing and salience attribution in relation to cannabis use

Aberrant attentional salience has been implicated in the cannabis-psychosis association. Here, history and frequency of cannabis use were examined against changes in overshadowing (OS), a cue competition paradigm that involves salience processing. Additionally, we examined the association between OS and alternative measures of aberrant salience, as well as schizotypy, in a non-clinical adult sample.280 participants completed an online geometry learning-based OS task, while a subset (N = 149) also completed the Salience Attribution Task (SAT) measure of aberrant salience. All completed the Schizotypal Personality Questionnaire (SPQ), Aberrant Salience Inventory (ASI), and the modified Cannabis Experience Questionnaire (CEQmv). Differences across OS and SAT performance stages and between cannabis use groups were assessed using mixed ANOVAs. Multiple regression and correlational analyses assessed the relationships between OS and SAT task metrics and SPQ and ASI subscale scores.Current cannabis users had significantly lower OS scores during the testing phase relative to those who do not use cannabis, at medium effect sizes. Schizotypy or ASI scores did not mediate this relationship. In the SAT, current cannabis users presented significantly higher implicit aberrant salience relative to non-users. Scores in the first training phase of the OS task significantly predicted higher explicit aberrant and adaptive salience scores in the SAT.These data indicate an association between regular cannabis use and abnormalities in cue competition effects in a healthy adult sample. Comparisons of OS and SAT cast new light on putative overlapping mechanisms underlying performance across different measures of salience.

A Framework for Video Summarization using Visual Attention Technique

Objectives: To develop an efficient Video Summarization technique that aims to utilize the saliency map for mimicking the human way of selecting the important events in the given video. Methods: This paper proposes Histogram based Weighted Fusion (HWF) algorithm that uses spatial and temporal saliency maps to act as guidance in creating the summary of the video. The spatial saliency score and temporal saliency score obtained from the corresponding saliency maps are fused using the proposed HWF algorithm to obtain the frame level importance score. It tries to depict the visual attention of the human brain when watching a particular video. Findings: The experimental results show that the proposed HWF algorithm performs better than the state-of-the-art methods. Novelty: The use of Histogram intersection and the incorporation of the exponential function as the weight for the combined feature enhance the summarization ability of the proposed model. Keywords: Video Summarization, Saliency Map, Histogram intersection, Contrast sensitivity function, Attention curves

Attention-based adaptive structured continuous sparse network pruning

Deep neural network models, especially CNNs, have a wide range of applications in many fields, but their high computational power requirements limit the deployment applications in many resource-constrained embedded devices. Pruning techniques reduce the computational power requirements of models by removing redundant structures from CNNs. Most existing static pruning methods use a global uniform pruning rate to prune pre-trained models and require finetuning to recover accuracy after pruning, resulting in high training costs, and the global uniform pruning rate is sub-optimal. While dynamic pruning methods perform pruning during training and use auxiliary modules to calculate the saliency scores of channels, but do not exploit its function of assisting network training. We propose an adaptive structured continuous sparse network pruning method based on the attention mechanism that prunes the original network during training. The attention-based channel similarity calculation module calculates the channel saliency scores while refining features to assist network training, and the adaptive continuous sparse control module gradually discretizes the channel saliency scores and assigns the pruning rate of each layer according to the preset pruning rate target. The pruned model is output after training and no additional fine-tuning is required. We validate the proposed method on CIFAR-10 and the large-scale dataset Imagenet using networks with different structures, and our method outperforms the comparative pruning methods at different pruning rates. In CIFAR-10, our method can reduce VGG-16 by 34.4% FLOPs while top-1 accuracy increased by 0.19%. In Imagenet, we can reduce ResNet-34 by 51.5% FLOPs, while the top-1 accuracy decreases by only 0.89%.

IJCM_115A: Assessing control status of diabetes and hypertension in adults in rural Nagpur: A Formative Approach

Background: Formative research is the process by which researchers aim to understand the current behaviours, perceptions and needs of the target population which affects their decisions and actions. There is gap in literature regarding the health system preparedness to tackle the increasing burden of lifestyle diseases, especially in post-COVID era. This study proposes to identify the various barriers and facilitators in optimising control status among those already diagnosed with lifestyle diseases, through formative approach. Objective: To describe the various barriers and facilitators important for maintaining optimum control of blood sugar and blood pressure among adults with diabetes and hypertension in rural Nagpur Methodology: Rural Nagpur (Populations served by RHTC Bela) Population: 1-Stakeholders including adult consenting diabetic and hypertensive patients. 2-Private and Govt. Medical Practitioner Sampling Technique: Purposive sampling of diabetic and Hypertensive patients was done based on their vocal and willingness to participate in Barrier Analysis. All health care providers from public health facilities (Medical officers and Community health officers from the selected PHC) were considered for in-depth interviews without any sampling. Data collection: Ethnographic analysis including Barrier Analysis through-semi- structured interview, free-listing and Pile sorting using standard techniques. Verbatim recording was followed by coding and analysis. Data Analysis: Analysis was done using Atlas. Ti and Anthropac Software. Smiths Saliency Score was generated wherever appropriate and cognitive map was created. Results: From interim analysis, through triangulation of data, some of the themes that emerged were “Frequent Refill”, “Healthcare Worker Reluctanc”, “Unavailability of medications” as the top barriers to maintaining good control status of diabetes and hypertension. Conclusion: This study will identify the barriers and facilitators at individual, family and at the health system level in controlling the lifestyle diseases, paving the way for the development of future implementation packages appropriate in the cultural context.

Health Equity in the Veterans Health Administration From Veterans’ Perspectives by Race and Sex

Advancing equitable patient-centered care in the Veterans Health Administration (VHA) requires understanding the differential experiences of unique patient groups. To inform a comprehensive strategy for improving VHA health equity through the comparative qualitative analysis of care experiences at the VHA among veterans of Black and White race and male and female sex. This qualitative study used a technique termed freelisting, an anthropologic technique eliciting responses in list form, at an urban academic VHA medical center from August 2, 2021, to February 9, 2022. Participants included veterans with chronic hypertension. The length of individual lists, item order in those lists, and item frequency across lists were used to calculate a salience score for each item, allowing comparison of salient words and topics within and across different groups. Participants were asked about current perceptions of VHA care, challenges in the past year, virtual care, suggestions for change, and experiences of racism. Data were analyzed from February 10 through September 30, 2022. The Smith salience index, which measures the frequency and rank of each word or phrase, was calculated for each group. Responses from 49 veterans (12 Black men, 12 Black women, 12 White men, and 13 White women) were compared by race (24 Black and 25 White) and sex (24 men and 25 women). The mean (SD) age was 64.5 (9.2) years. Some positive items were salient across race and sex, including "good medical care" and telehealth as a "comfortable/great option," as were some negative items, including "long waits/delays in getting care," "transportation/traffic challenges," and "anxiety/stress/fear." Reporting "no impact" of racism on experiences of VHA health care was salient across race and sex; however, reports of race-related unprofessional treatment and active avoidance of race-related conflict differed by race (present among Black and not White participants). Experiences of interpersonal interactions also diverged. "Impersonal/cursory" telehealth experiences and the need for "more personal/attentive" care were salient among women and Black participants, but not men or White participants, who associated VHA care with courtesy and respect. In this qualitative freelist study of veteran experiences, divergent experiences of interpersonal care by race and sex provided insights for improving equitable, patient-centered VHA care. Future research and interventions could focus on identifying differences across broader categories both within and beyond race and sex and bolstering efforts to improve respect and personalized care to diverse veteran populations.

Clinical researchers’ insights on key data for eligibility screening in clinical studies

Abstract Introduction: Clinical research is critical for healthcare advancement, but participant recruitment remains challenging. Clinical research professionals (CRPs; e.g., clinical research coordinator, research assistant) perform eligibility prescreening, ensuring adherence to study criteria while upholding scientific and ethical standards. This study investigates the key information CRP prioritizes during eligibility prescreening, providing insights to optimize data standardization, and recruitment approaches. Methods: We conducted a freelisting survey targeting 150 CRPs from diverse domains (i.e., neurological disorders, rare diseases, and other diseases) where they listed essential information they look for from medical records, participant/caregiver inquiries, and discussions with principal investigators to determine a potential participant’s research eligibility. We calculated the salience scores of listed items using Anthropac, followed by a two-level analytic procedure to classify and thematically categorize the data. Results: The majority of participants were female (81%), identified as White (44%) and as non-Hispanic (64.5%). The first-level analysis universally emphasized age, medication list, and medical history across all domains. The second-level analysis illuminated domain-specific approaches in information retrieval: for instance, history of present illness was notably significant in neurological disorders during participant and principal investigator inquiries, while research participation was distinctly salient in potential participant inquiries within the rare disease domain. Conclusion: This study unveils the intricacies of eligibility prescreening, with both universal and domain-specific methods observed. Variations in data use across domains suggest the need for tailored prescreening in clinical research. Incorporating these insights into CRP training and refining prescreening tools, combined with an ethical, participant-focused approach, can advance eligibility prescreening practices.

Siamese-based offline word level writer identification in a reduced subspace

In this paper, we explore the notion of similarity learning by utilizing the Siamese neural network employing the residual framework for the purpose of writer identification based on offline handwritten input word images. Apart from being text-independent, our method does not impose any limitations on the number of characters of the input word image being employed also it can be used in real-world applications where input image patches with a few letters exist. The novelty in our proposal is in the exploration of a sparse-based model for representing the output feature vector of the Siamese network in a reduced dimensional space. We also formulate a divergence-based approach for assigning a saliency score to each component in the sparse representation based on their discriminatory power. The system efficacy has been demonstrated on well-known word-level databases and the results obtained are promising when compared with previous works.

Assessment of Occupational Burnout And Its Determinants, Causes of Occupational Stress, And Its Coping Strategies Among Nurses Working In The Psychiatric Wards: A mixed-method study

Aims: This study assessed occupational burnout and its determinants, causes of occupational stress, and its coping strategies amongst 13 nurses working in the psychiatry wards in India. Methods: This was a longitudinal mixed-method study. The qualitative component comprised interviewing each participant under the steps of “free listing” and “pile sorting” to assess the causes of occupational stress and its coping strategies. The occupational burnout experiences were assessed using the Maslach Burnout Inventory. Results: Low-level depersonalization scores were present among six (46.1%) participants. Scores under this component of burnout were negatively correlated with years of work experience in providing nursing care for admitted psychiatric patients (rs = -0.548, p=0.05). Median scores of the emotional exhaustion scale (p=0.047) and of the depersonalization scale (p=0.016) were significantly higher among participants working at the government hospital. The major cause of occupational stress was poor infrastructure and treatment facilities with a salience score of 0.154. The most adopted strategies to tide over occupational stress were reading books and meditation with a salience score of 0.128 and 0.109 respectively. Conclusion: Extended work experience of participants was associated with their better ability to understand and empathize more with the patients as reflected by the low depersonalization scores. Two components of occupational burnout were found to be higher among nurses working in the government hospital. Organizational relationships were a more common cause of occupational stress than client-related matters among the participants. Recreational activities were the most common coping strategies adopted by the participants to deal with stress.

Query-Guided Refinement and Dynamic Spans Network for Video Highlight Detection and Temporal Grounding in Online Information Systems

With the surge in online video content, finding highlights and key video segments have garnered widespread attention. Given a textual query, video highlight detection (HD) and temporal grounding (TG) aim to predict frame-wise saliency scores from a video while concurrently locating all relevant spans. Despite recent progress in DETR-based works, these methods crudely fuse different inputs in the encoder, which limits effective cross-modal interaction. To solve this challenge, the authors design QD-Net (query-guided refinement and dynamic spans network) tailored for HD&TG. Specifically, they propose a query-guided refinement module to decouple the feature encoding from the interaction process. Furthermore, they present a dynamic span decoder that leverages learnable 2D spans as decoder queries, which accelerates training convergence for TG. On QVHighlights dataset, the proposed QD-Net achieves 61.87 HD-HIT@1 and 61.88 TG-mAP@0.5, yielding a significant improvement of +1.88 and +8.05, respectively, compared to the state-of-the-art method.

Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19

Healthcare delivery during the initial days of outbreak of COVID-19 pandemic was badly impacted due to large number of severely infected patients posing an unprecedented global challenge. Although the importance of Chest X-rays (CXRs) in meeting this challenge has now been widely recognized, speedy diagnosis of CXRs remains an outstanding challenge because of fewer Radiologists. The exponential increase in Smart Phone ownership globally, including LMICs, provides an opportunity for exploring AI-driven diagnostic tools when provided with large volumes of CXRs transmitted through Smart Phones. However, the challenges associated with such systems have not been studied to the best of our knowledge. In this paper, we show that the predictions of AI-driven models on CXR images transmitted through Smart Phones via applications, such as WhatsApp, suffer both in terms of Predictability and Explainability, two key aspects of any automated Medical Diagnosis system. We find that several existing Deep learning based models exhibit prediction instability–disagreement between the prediction outcome of the original image and the transmitted image. Concomitantly we find that the explainability of the models deteriorate substantially, prediction on the transmitted CXR is often driven by features present outside the lung region, clearly a manifestation of Spurious Correlations. Our study reveals that there is significant compression of high-resolution CXR images, sometimes as high as 95%, and this could be the reason behind these two problems. Apart from demonstrating these problems, our main contribution is to show that Multi-Task learning (MTL) can serve as an effective bulwark against the aforementioned problems. We show that MTL models exhibit substantially more robustness, 40% over existing baselines. Explainability of such models, when measured by a saliency score dependent on out-of-lung features, also show a 35% improvement. The study is conducted on WaCXR dataset, a curated dataset of 6562 image pairs corresponding to original uncompressed and WhatsApp compressed CXR images. Keeping in mind that there are no previous datasets to study such problems, we open-source this data along with all implementations.

THE MEASURE OF TARGET SALIENCY FOR TARGET-ORIENTED IMAGE FUSION

Our analysis of the available literature on quantitative quality indices characterizing the visibility, conspicuity, and saliency of the objects in an image reveals that the used approaches do not fit the military needs, in particular those required by the target sightseeing systems of armored vehicles. Their main drawback is that these indices are not target-oriented. The long elapsed time needed for their evaluation is another inconvenience, making them inapplicable for military purposes, which in most cases require a real-time response. Special target-oriented image quality indices are needed for military applications. In this paper, we propose the very such target-oriented normalized visibility, conspicuity, and saliency indices and corresponding methods for their measurements. Comparison of the times elapsed for the generation of saliency maps by the analyzed literature methods and the adequacy of the saliency scores produced by them with those for the saliency index, proposed in this paper, reveals that the computation with our approach is several orders of magnitude faster than for the prototype methods available in the literature while providing comparable adequacy of the measured target-oriented image quality indices.

Impaired cognitive control moderates the relation between the attribution of incentive salience and severity of consumption in patients with methamphetamine dependence

BackgroundCognitive control and the attribution of incentive salience are two key neuropsychological processes proposed to explain substance use disorder (SUD). However, little is known about how they interact to influence the severity of drug use in people with SUD. ObjectiveTo determine if cognitive control exerts a moderating effect on the relation between the attribution of salience to drug/reward-related cues and the severity of drug use in SUD cases. MethodSixty-nine SUD cases with methamphetamine as the main drug of consumption were selected and evaluated. Participants performed the Stroop, Go/No-Go, and Flanker tasks to identify a latent cognitive control factor, and the Effort-Expenditure for Reward task, as well as answering the Methamphetamine Incentive Salience Questionnaire to measure the attribution of incentive salience. Severity of drug use was determined by the KMSK scale and an exploratory clinical interview. ResultsAs expected, higher incentive salience attribution predicted greater severity of methamphetamine use. Unexpectedly, however, we found a moderating effect of impaired cognitive control on the relations between higher incentive salience scores and higher monthly drug use, and between younger age at onset of systematic drug use and higher incentive salience scores. ConclusionResults show the moderating role of cognitive control on the relation between incentive salience attribution and severity of drug use in SUD cases, and help explain the chronic, relapsing nature of addiction, knowledge necessary to develop more precise prevention and treatment strategies.

What about the Latent Space? The Need for Latent Feature Saliency Detection in Deep Time Series Classification

Saliency methods are designed to provide explainability for deep image processing models by assigning feature-wise importance scores and thus detecting informative regions in the input images. Recently, these methods have been widely adapted to the time series domain, aiming to identify important temporal regions in a time series. This paper extends our former work on identifying the systematic failure of such methods in the time series domain to produce relevant results when informative patterns are based on underlying latent information rather than temporal regions. First, we both visually and quantitatively assess the quality of explanations provided by multiple state-of-the-art saliency methods, including Integrated Gradients, Deep-Lift, Kernel SHAP, and Lime using univariate simulated time series data with temporal or latent patterns. In addition, to emphasize the severity of the latent feature saliency detection problem, we also run experiments on a real-world predictive maintenance dataset with known latent patterns. We identify Integrated Gradients, Deep-Lift, and the input-cell attention mechanism as potential candidates for refinement to yield latent saliency scores. Finally, we provide recommendations on using saliency methods for time series classification and suggest a guideline for developing latent saliency methods for time series.

Perception about substance abuse among the medical students in Visakhapatnam, India: a qualitative research

Background: Substance abuse is an emerging disorder of global concern. In India about 14.6% of the population (between 10 and 75 year of age) uses alcohol. Also, 28.6 percent (266.8 million) of adults in India, aged 15 and above currently use tobacco in some form. Youth period is an important transition period where students may begin to consume alcohol, tobacco, and other substances. Methods: A Qualitative approach using systematic methods i.e., Free listing & Pile sorting, Conducted among third professional year students in a Medical College of North coast Andhra Pradesh. Analysed by Cultural domain analysis using Visual Anthropac Pile sort version1.0.1.36 based on salience scores. Results: A total of 13 medical students were included. Among them 9 (69%) were females and 4(31%) were males. Stress was found to be the major cause for substance abuse with salience score of 0.418, followed by peer pressure (0.402). The solutions for among them parental guidance is the best solution for substance abuse with highest salience score of 0.412, followed by awareness about consequences (0.409). Conclusions: Our study reveals that it is necessary to bring positive behavioural changes through adoption of comprehensive interventions, counselling methods and health education in medical universities.

Impact of COVID-19 on community medicine education and training-postgraduates’ perspective: a qualitative exploration

Background: World Health Organization on March 11, 2020 announced, the coronavirus disease 2019 (COVID-19) as pandemic. It has affected nearly every nation with remarkable alterations on everyday life. COVID-19 has had heartfelt impact on the medical education worldwide. Closure of the medical schools and universities has disturbed the education of future health professionals. During this pandemic, most educational institutions have opted for online education to protect their students. Online education has been gaining encouragement in almost all countries around the world. This coincides with the recently introduced competency-based medical education in India which has adopted online education. This created a new challenge for the institutions involved, the instructors and the students since they must adapt quickly to this new mode of learning. Methods: A Qualitative approach using Systematic methods (Free listing and Pile sorting) was done among postgraduate students during the month of September 2021. A total of 32 postgraduates were included in the study. Results: A total of 32 postgraduates were included. Among them 23 (72%) were females and 9 (28%) were males. According to salience score, effects of COVID on medical education in postgraduates’ perspective in decreasing order are loss of academics, reduced clinical exposure, less student teacher interaction. One positive effect is new era of E-learning. Pile sorting resulted into two piles academic loss and technical issues. Conclusion: COVID-19 has created a lot of impact on medical education creating psychological stress more due to online education and academics are hampered due to COVID duties.