Assessing healthcare workers’knowledge and confidence in the diagnosis,management and prevention of Monkeypox

BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confidence in the diagnosis and management of Mpox.METHODS We conducted a cross-sectional study via an online survey designed mainly from the World Health Organization course distributed among Burundi HCWs from June-July 2023.The questionnaire comprises 8 socioprofessional-related questions,22 questions about Mpox disease knowledge,and 3 questions to assess confidence in Mpox diagnosis and management.The data were analyzed via SPSS software version 25.0.A P value<0.05 was considered to indicate statistical significance.RESULTS The study sample comprised 471 HCWs who were mainly medical doctors(63.9%)and nurses(30.1%).None of the 22 questions concerning Mpox knowledge had at least 50%correct responses.A very low number of HCWs(17.4%)knew that Mpox has a vaccine.The confidence level to diagnose(21.20%),treat(18.00%)or prevent(23.30%)Mpox was low among HCWs.The confidence level in the diagnosis of Mpox was associated with the HCWs’age(P value=0.009),sex(P value<0.001),work experience(P value=0.002),and residence(P value<0.001).The confidence level to treat Mpox was significantly associated with the HCWs’age(P value=0.050),sex(P value<0.001),education(P value=0.033)and occupation(P value=0.005).The confidence level to prevent Mpox was associated with the HCWs’education(P value<0.001),work experience(P value=0.002),residence(P value<0.001)and type of work institution(P value=0.003).CONCLUSION This study revealed that HCWs have the lowest level of knowledge regarding Mpox and a lack of confidence in the ability to diagnose,treat or prevent it.There is an urgent need to organize continuing medical education programs on Mpox epidemiology and preparedness for Burundi HCWs.We encourage future researchers to assess potential hesitancy toward Mpox vaccination and its associated factors.

Knowledge of Hospital Acquired Infections (HAIs) among Medical Students in a Tertiary Hospital in Jos North Local Government Area, Plateau State, Nigeria

Background: Hospital Acquired Infections (HAIs) remain a common cause of death, functional disability, emotional suffering and economic burden among hospitalized patients. Knowledge of HAIs is important in its prevention and control. This study seeks to assess the knowledge of Hospital Acquired Infections (HAIs) among medical students in a Tertiary Hospital in Jos North Local Government Area, Plateau State, Nigeria. Methods: This was a descriptive cross-sectional study done in October 2019 among clinical medical students using a Multistage sampling technique. Data was collected using a self-administered structured questionnaire and analyzed using the IBM SPSS 20 (Statistical Package for the Social Sciences). Ethical approval was granted by Bingham University Teaching Hospital, Ethics Committee, Jos, Plateau State. Results: A total of 219 students in the clinical arm of the College of Medicine and Health Sciences were selected. A higher proportion (97.7%) of respondents knew about Hospital Acquired Infections and 85.4% knew that Hospital Acquired infections occur in the hospital, and (86.3%) considered patients contagious with half (58.9%) considered patients as the most important source of HAIs, followed by care givers (13.2%), then doctors including medical students and interns (10.0%) and lastly nurses (8.7%). The majority of respondents (70.8%) considered Surgical Wound Infections to be the most commonly occurring HAI, followed by UTIs (69.9%), RTIs (61.2%), BSIs (37.0%) and others (0.9%). The clinical thermometer was the instrument that most commonly transmits HAIs (82.6%), then followed by stethoscope (62.1%), white coats (53.9%), and blood pressure cuff (51.1%). Most respondents knew the infectious substances, like blood (96.3%), nasal discharge (82.6%), saliva (85.3%), and faeces (79.4%) transmitted HAIs, 72.6% of the respondents said that they were aware of the recommended hand washing techniques by WHO. Conclusion: The majority of students 91.3% had good knowledge while 8.7% had poor knowledge of HAIs. Lower classes had more respondents with poor knowledge. This finding was statistically significant (p = 0.002, Chi-square 12.819). Students are encouraged to keep up the level of knowledge they have about HAIs. These students can help improve the knowledge of those whose knowledge level is low. Government and NGOs should support sponsorship for capacity-building events targeted at HAIs for healthcare workers and medical students.

Generation of scale-free knowledge network with local world mechanism

In order to simulate the real growing process, a new type of knowledge network growth mechanism based on local world connectivity is constructed. By the mean-field method, theoretical prediction of the degree distribution of the knowledge network is given, which is verified by Matlab simulations. When the new added node＇s local world size is very small, the degree distribution of the knowledge network approximately has the property of scale-free. When the new added node＇s local world size is not very small, the degree distribution transforms from pure power-law to the power-law with an exponential tailing. And the scale-free index increases as the number of new added edges decreases and the tunable parameters increase. Finally, comparisons of some knowledge indices in knowledge networks generated by the local world mechanism and the global mechanism are given. In the long run, compared with the global mechanism, the local world mechanism leads the average knowledge levels to slower growth and brings homogenous phenomena.

Heterogeneous Image Knowledge Driven Visual Perception

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous images.Facing the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Immune evolutionary algorithms with domain knowledge for simultaneous localization and mapping

Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Local Knowledge on the Influence of Land Use/Cover Changes and Conservation Threats on Avian Community in the Kilombero Wetlands, Tanzania

Local knowledge of the history and ecology of wetland ecosystems is very useful in wetland resources management, especially when other historical ecological information is not available and can be integrated with scientific knowledge to introduce better management of resources. The aims of this paper were to assess existing local knowledge on land use/cover changes in the Kilombero wetlands, thereafter investigate local knowledge on its effect on avian population in the wetland and identify factors influencing local knowledge on such changes in the study area. Random sampling was used to obtain representative sample population for this study. Structured questionnaire and focus group discussions were used to extract information from local people in six villages. Study results from multi-response analysis showed that natural forests had been converted into cropland and bushed grassland, grassland to crop land, grassland to grazed land, forest to settlement and grassland to settlement. Land use change was singled out as primary cause of decrease in avian community in the wetland. Threats to the conservation of avian species were identified as livestock grazing, drought, use of poison, traps and bush meat hunting for food. Age and education level were seen as determinants of household’s knowledge on the ecological changes. This pool of existing knowledge is important among wetland users and stakeholders in order to generate conservation strategies of the wetland ecosystem.

Climate Change Impacts, Local Knowledge and Coping Strategies in the Great Ruaha River Catchment Area, Tanzania

Climate change has profound implications for managing freshwater resources and species dependent on those resources. Water is an essential component of the life support system of the earth, and a basic resource for socio-economic development. The Great Ruaha River Catchment Area is a dynamic and complex ecosystem requiring inclusion climate change adaptation in the management of the freshwater and natural resources available to reduce the severity of climate change impacts. Rainfall has decreased considerably during the last 10 - 30 years, and characterised by high interannual variability, seasonal shifts and variable seasonal distribution with unpredictable onset and ending of rains and shortened growing seasons. Temperature has increased considerably during this period causing increased evapotranspiration losses and incidences of pest and diseases. The freshwater of Ruaha River and it tributaries are vulneable to changing climate, such as drought, which can negatively impact on the livelihoods of the people through de- creased crop and livestock production, and on local biodiversity. The changing climate has had negative impacts on, among other aspects, land use and water shortages for irrigation, livestock and domestic uses. This has compelled riparian communities in the catchment to devises coping strategies including practicing irrigation to provide supplementary water to crops, using drought tolerant crop varieties, rationing of irrigation water in farmlands, wetland cultivation, and diversification to non-agricultural activities. Despite the existence of many indicators used for local climate forecasting, there are limitations to local adaptation, including among others, poverty, institutional aspects and limited integration of climate adaptation in various sectors. The bulk of indigenous knowledge could be integrated into formal adaptation planning, and may be important components of environmental conservation at the local level.

A new evolutional model for institutional field knowledge flow network

Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.

Nutrition Awareness and Knowledge and Body Mass Index in Adolescents in Public Junior Secondary Schools

Background: Non-communicable diseases are increasing among adolescents. The decision about diet is the foundation of eating habits that could persist to adulthood. Dietary decisions, which usually are hard to change later in life, make nutrition education at school paramount to prevent obesity and NCDs, and promote healthy eating. Objectives: To assess level of nutrition awareness and knowledge of healthy eating and food intake behaviors and association with Body Mass Index (BMI) and age. Methods: A cross sectional study that included measures such as age, gender, socioeconomic status, BMI, and nutrition knowledge was conducted in 264 respondents from 18th June 2015 to 9th July 2015. The nutrition knowledge questionnaire was composed of 24 questions divided into food nutrients, food contents, healthiest foods, and energy expenditure and nutrition benefits. CDC BMI chart for 2-20-year-olds was used to plot respondent’s weight and height. Results: The mean age of the respondents was 14.3 years (SD 0.79). Most of the respondents (153/252, 53.6%) had a low socio-economic status as categorized by the present study. The mean (SD) BMI was 20.08 (3.90). Most respondents (56.3%, 142/252) failed the knowledge test and scored below 50% and only two respondents (0.8%) had excellent nutrition knowledge. The mean percentage achieved was 46.1% (SD 15.91) ranging from 8.3% to 87.5%. There was a significant correlation between nutrition knowledge and BMI (p = 0.001). Conclusion: The study shows that adolescents do not have adequate nutrition knowledge, placing them at risk for developing non-communicable diseases later in life. Nutrition education programs are urgently needed for teachers, parents, and children.

GATiT:An Intelligent Diagnosis Model Based on Graph Attention Network Incorporating Text Representation in Knowledge Reasoning

The growing prevalence of knowledge reasoning using knowledge graphs(KGs)has substantially improved the accuracy and efficiency of intelligent medical diagnosis.However,current models primarily integrate electronic medical records(EMRs)and KGs into the knowledge reasoning process,ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the text.To better integrate EMR text information,we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning(GATiT),which comprises text representation,subgraph construction,knowledge reasoning,and diagnostic classification.In the text representation process,GATiT uses a pre-trained model to obtain text representations of the EMRs and additionally enhances embeddings by including chief complaint information and numerical information in the input.In the subgraph construction process,GATiT constructs text subgraphs and disease subgraphs from the KG,utilizing EMR text and the disease to be diagnosed.To differentiate the varying importance of nodes within the subgraphs features such as node categories,relevance scores,and other relevant factors are introduced into the text subgraph.Themessage-passing strategy and attention weight calculation of the graph attention network are adjusted to learn these features in the knowledge reasoning process.Finally,in the diagnostic classification process,the interactive attention-based fusion method integrates the results of knowledge reasoning with text representations to produce the final diagnosis results.Experimental results on multi-label and single-label EMR datasets demonstrate the model’s superiority over several state-of-theart methods.

Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design.This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph.Specifically,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design.Moreover,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction module.Experimental comparison verified the effectiveness and accuracy of the proposed knowledge extraction model.The case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

A Survey of Knowledge Graph Construction Using Machine Learning

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

Reflective practice and knowledge development: Transforming research for a practice-based discipline

Knowledge development to guide evidence-informed practice is a cornerstone of nursing as a practice-based discipline.The emphasis on empirical knowledge development overshadows other ways of knowledge developmentdpersonal,aesthetic,and ethical.Technical,objective knowledge development is more dominant than knowledge development for delivering holistic,personcentered care.Personal,aesthetic,and ethical ways of knowing are essential factors in satisfying work environments,patient satisfaction,and nurse retention.Boyer's model of scholarship development defining the scholarship of discovery,teaching,application,and integration guide nurses in building programs of scholarship informing the practice of nursing in practice and academia with an aim of improving and transforming healthcare delivery and patient outcomes.The purpose of this paper is to describe the various forms of scholarship described by Boyer as priorities in knowledge development,examine how the multiple ways of knowing expand traditional empirical perspectives of knowledge development,and present the value of reflective practices that undergird knowledge generation,integration,and application for holistic personcentered safe quality care.Reflective practices have a unique contribution to forming the unique art and science of nursing as a practice-based discipline.

Knowledge, Attitudes and Perceptions of Colorectal Cancer Screening in Brazzaville

Objective: assess the knowledge, attitudes, and perceptions of Brazzaville residents on colorectal cancer and its screening. Population and Methods: a CAP-type cross-sectional study was conducted from June 1 to October 31, 2022, with 803 workers approached at their place of service. Information was collected using a questionnaire administered to participants. The variables studied concerned knowledge, attitudes, and perceptions about colorectal cancer. SPSS software, along with Chi-square and Fisher tests, was used for data entry and analysis. Odds ratios were calculated to determine the strength of the association between variables. Results: The average age of the participants was 33.5 ± 10 years, with a sex ratio of 0.9. There were 231 health workers. The main sources of information were health personnel (78.2%) and the internet (52.6%). The site of the pathology was known to 87% of participants. About 40% identified age, genetic predispositions, and a diet rich in animal fats as risk factors. No signs of the disease were known by 50% of the participants. Colonoscopy was known as a screening method by 40% of participants. Seventy-five percent were willing to participate in a CRC awareness campaign, but only 5% agreed to a screening colonoscopy, although 96% recognized its usefulness. Overall, the level of knowledge was insufficient in 70.4% of cases;attitudes were adapted in 55.7% of cases, and perceptions were adapted in 97.3% of cases. Factors influencing knowledge included young age (p = 0.006), a good level of education, being a healthcare worker, and high socio-economic level. Conclusion: colorectal cancer and its screening are poorly understood by the population. Awareness activities must be organized to improve knowledge and promote screening and early diagnosis of CRC.

Assessment of Knowledge, Attitudes, and Practices Regarding Hepatitis B among Students at Gaston Berger University of Saint-Louis in 2024

Introduction: Hepatitis B is a public health problem. This study aimed to investigate the knowledge, attitudes, and practices of students at Gaston Berger University of Saint-Louis regarding hepatitis B. Methods: A descriptive cross-sectional study was conducted in March 2024 within the university’s social campus among a sample of 800 students. Results: The sex ratio favored men at 1.5, and the average age was 23.3 years with a standard deviation of 2.7 years. Among students, 26.6% had never heard of the term “hepatitis B”, and 60% knew it was a liver disease. Young people and adolescents were identified as the most exposed population group by 52.3% of respondents. According to 53.7% of the sample, hepatitis B would be a curable disease. Furthermore, 95.0% of the surveyed individuals were not vaccinated against hepatitis B, and 36.3% were afraid of living with an infected person. While 63.0% had poor knowledge, 78.8% had negative attitudes, and poor practices were observed in 6.9% of participants. Conclusion: This study highlighted a lack of knowledge and concerning attitudes among students regarding hepatitis B. The results underscore the need to intensify information and awareness campaigns about this disease, as well as the importance of promoting vaccination and encouraging appropriate preventive practices among the student population.

Reviewer Acknowledgements

The editors of International Journal of Ophthalmology gratefully acknowledge the members of IJO Editorial Board and reviewers from 57 countries and regions who participated in the peer-reviews and provided their valuable comments between Nov.1^(st),2022 and Oct.31^(st),2023.

A novel fracture-cavity reservoir outcrop geological knowledge base construction method considering parameter collection and processing,mutual transformation of data-knowledge,application and update

This study endeavors to formulate a comprehensive methodology for establishing a Geological Knowledge Base(GKB)tailored to fracture-cavity reservoir outcrops within the North Tarim Basin.The acquisition of quantitative geological parameters was accomplished through diverse means such as outcrop observations,thin section studies,unmanned aerial vehicle scanning,and high-resolution cameras.Subsequently,a three-dimensional digital outcrop model was generated,and the parameters were standardized.An assessment of traditional geological knowledge was conducted to delineate the knowledge framework,content,and system of the GKB.The basic parameter knowledge was extracted using multiscale fine characterization techniques,including core statistics,field observations,and microscopic thin section analysis.Key mechanism knowledge was identified by integrating trace elements from filling,isotope geochemical tests,and water-rock simulation experiments.Significant representational knowledge was then extracted by employing various methods such as multiple linear regression,neural network technology,and discriminant classification.Subsequently,an analogy study was performed on the karst fracture-cavity system(KFCS)in both outcrop and underground reservoir settings.The results underscored several key findings:(1)Utilization of a diverse range of techniques,including outcrop observations,core statistics,unmanned aerial vehicle scanning,high-resolution cameras,thin section analysis,and electron scanning imaging,enabled the acquisition and standardization of data.This facilitated effective management and integration of geological parameter data from multiple sources and scales.(2)The GKB for fracture-cavity reservoir outcrops,encompassing basic parameter knowledge,key mechanism knowledge,and significant representational knowledge,provides robust data support and systematic geological insights for the intricate and in-depth examination of the genetic mechanisms of fracture-cavity reservoirs.(3)The developmental characteristics of fracturecavities in karst outcrops offer effective,efficient,and accurate guidance for fracture-cavity research in underground karst reservoirs.The outlined construction method of the outcrop geological knowledge base is applicable to various fracture-cavity reservoirs in different layers and regions worldwide.

Assessment of Knowledge, Attitude and Practices among Healthcare Workers in a Tertiary Care Hospital on Needle Stick Injury

Purpose: Needle-stick injury (NSI) is one of the most potential occupational hazards for healthcare workers because of the transmission of blood-borne pathogens. As per recent data, around 30 lakh healthcare workers sustain Needle stick injuries each year. This study was conducted to assess healthcare workers’ knowledge, attitude and practices regarding needle stick injury. Materials & Methods: A cross-sectional study was conducted in a Tertiary Care Hospital over the period of 3 months. The study population consisted of Intern Doctors, Post Graduate resident Doctors, Staff Nurses, laboratory technicians of Government Medical College and New Civil Hospital, Surat (n = 300). The data were collected using a self-administered questionnaire via the means of Google Forms. Questionnaire was made with prior review literature. The data obtained were entered and analysed in Microsoft Excel. Results: The prevalence of NSI in our study was 46%, with a higher prevalence among the PG residents (72%). Overall scores regarding knowledge and attitude were better in PG residents (knowledge score > 7 in 71% and Attitude Score > 7 in 68% of PG Residents). Even though the PG residents scored highest in the knowledge category, the majority of them suffered needle stick injuries as a result of poor practice scores. Among those who had NSI (n = 139/300), 70% of study participants had superficial injuries, only 9% reported the incident, 18% got medical attention within 2 hours of the incident, and 7% followed up to recheck their viral markers status. Most incidents of NSI were due to hypodermic needles while recapping needles. Conclusion: Exposure to needle stick injuries and their underreporting remains a common problem. It is imperative that healthcare workers receive regular training on the proper handling of sharp objects. We can also draw the conclusion that preventing NSIs requires putting knowledge into practice.