Field Of Health Care Research Articles

Canadian National Pancreas Conference 2023: A Review of Multidisciplinary Engagement in Pancreatic Cancer Care.

Pancreatic cancer is a complex malignancy associated with poor prognosis and high symptom burden. Optimal patient care relies on the integration of various sectors in the healthcare field as well as innovation through research. The Canadian National Pancreas Conference (NPC) was co-organized and hosted by Craig's Cause Pancreatic Cancer Society and The Royal College of Physicians and Surgeons in November 2023 in Montreal, Canada. The conference sought to bridge the gap between Canadian healthcare providers and researchers who share the common goal of improving the prognosis, quality of life, and survival for patients with pancreatic cancer. The accredited event featured discussion topics including diagnosis and screening, value-based and palliative care, pancreatic enzyme replacement therapy, cancer-reducing treatment, and an overview of the current management landscape. The present article reviews the NPC sessions and discusses the presented content with respect to the current literature.

Two-dimensional p-n heterojunctions of black phosphorus nanosheet-sensitized α-MoO3 nanoflake for low-temperature chemiresistive NH3 recognition

Of diverse NH3 gas-detection strategies applied in the fields of individual healthcare and industrial production, chemiresistive gas sensors gain the most popularity owing to the superior merits of low cost, convenient signal processing and high sensitivity. However, severe challenges still exist such as elevated operation temperature (>200 °C), limited sensitivity and unsatisfactory selectivity especially under low-concentration and high-humidity conditions. To overcome these hindrances, a sensing layer of black phosphorus nanosheets (BP)-modified α-MoO3 nanoflakes was prepared in this work. After subtle optimization of ingredient composition and operation temperature, the composite sensor S2 could recognize NH3 gas with the concentration spanning from 0.4 to 25 ppm at 40 °C. In particular, a larger mean response (27 % vs. ∼ 2.3 %) with a response/recovery speed of 275/388 s toward 10 ppm NH3 and lower detection of limit (LoD, 0.4 ppm vs. 10 ppm) was achieved with respect to pure BP counterpart. In addition, excellent repeatability, selectivity, long-term stability and humidity-tolerant features were demonstrated for Sensor S2. The improved sensing performance after α-MoO3 incorporation was primarily ascribed to abundant p-n heterojunctions, excellent conductivity of BP and hydrophilic MoO3 that mitigated the water corrosion effect on susceptible BP nanosheets. This work offers an alternative strategy for sensitive and low-temperature NH3 detection, and well cater for the demanding requirements of high sensitivity and low-power consumption in the field of novel gas sensors applicable in the future Internet of Things and exhaled breath-oriented medical diagnosis.

Unveiling the bright side and dark side of AI-based ChatGPT : a bibliographic and thematic approach

ABSTRACT The current research endeavour aims to examine the most recent advancements pertaining to AI-powered ChatGPT in scholarly literature. Moreover, this research examines both the positive and negative aspects of ChatGPT utilisation across several sectors including business, research, and society. The data was collected from Scopus, using the Preferred Reporting Items for Systematic and Meta-Analysis (PRISMA) methodology. The process of scientific mapping was carried out, wherein both biblometric and thematic analysis was conducted. The results suggest that there is a significant amount of research being conducted on the subject, particularly in the fields of healthcare and education. Thematic analysis reveals a wide range of research issues, including the examination of the impact of technology on decision-making processes and the utilisation of ChatGPT to address complex business challenges. Theoretical perspectives underscore the significance of ethical deliberations, regulatory structures, interdisciplinary cooperation, and user instruction in the advancement and implementation of Artificial Intelligence systems.

Advanced Deep Learning Fusion Model for Early Multi-Classification of Lung and Colon Cancer Using Histopathological Images.

In recent years, the healthcare field has experienced significant advancements. New diagnostic techniques, treatments, and insights into the causes of various diseases have emerged. Despite these progressions, cancer remains a major concern. It is a widespread illness affecting individuals of all ages and leads to one out of every six deaths. Lung and colon cancer alone account for nearly two million fatalities. Though it is rare for lung and colon cancers to co-occur, the spread of cancer cells between these two areas-known as metastasis-is notably high. Early detection of cancer greatly increases survival rates. Currently, histopathological image (HI) diagnosis and appropriate treatment are key methods for reducing cancer mortality and enhancing survival rates. Digital image processing (DIP) and deep learning (DL) algorithms can be employed to analyze the HIs of five different types of lung and colon tissues. Therefore, this paper proposes a refined DL model that integrates feature fusion for the multi-classification of lung and colon cancers. The proposed model incorporates three DL architectures: ResNet-101V2, NASNetMobile, and EfficientNet-B0. Each model has limitations concerning variations in the shape and texture of input images. To address this, the proposed model utilizes a concatenate layer to merge the pre-trained individual feature vectors from ResNet-101V2, NASNetMobile, and EfficientNet-B0 into a single feature vector, which is then fine-tuned. As a result, the proposed DL model achieves high success in multi-classification by leveraging the strengths of all three models to enhance overall accuracy. This model aims to assist pathologists in the early detection of lung and colon cancer with reduced effort, time, and cost. The proposed DL model was evaluated using the LC25000 dataset, which contains colon and lung HIs. The dataset was pre-processed using resizing and normalization techniques. The model was tested and compared with recent DL models, achieving impressive results: 99.8% for precision, 99.8% for recall, 99.8% for F1-score, 99.96% for specificity, and 99.94% for accuracy. Thus, the proposed DL model demonstrates exceptional performance across all classification categories.

Applications of AI in martial arts: A survey

Artificial intelligence (AI) has become an interdisciplinary subject that integrates digital image processing, machine learning, and computer science, etc. A lot of work in martial arts benefits from AI technology. In this paper, we highlight and summarize the applications of AI in martial arts and indicate the main progress of related studies. Through the investigation of martial arts styles, tasks, data acquisition methods, and algorithm improvements, a paradigm that represents a possible technological process for an intelligent martial arts training system was summarized. These applications bring more scientific training and more effective technical analysis to martial arts. Furthermore, this paper delves into specific AI applications in areas such as action recognition, pose estimation, action evaluation, elite athlete support, sports betting, health care, animation generation, and competition field segmentation. By exploring these areas, the research prospects of AI applications in martial arts are comprehensively discussed. This paper inspires further research on martial arts by using AI, which includes helping researchers expand their research ideas and research methods, identify frontier issues, and innovate based on existing research.

A zero-knowledge proof federated learning on DLT for healthcare data

With the increasingly widespread adoption of Healthcare 4.0 practices, new challenges have arisen for the utilization of collected sensitive data. On the one hand, these data have immense potential to unlock valuable insights for personalized medicine, early disease detection, and predictive analysis thanks to the use of Artificial Intelligence. On the other hand, ensuring the protection of patient privacy is of paramount importance to maintain trust and uphold ethical practices within the healthcare system. Classical centralized learning approaches do not fit well with the privacy and security requirements imposed by the law and the sensitivity of treated data, which is why decentralized learning approaches are gaining ground. Among these, Federated Learning (FL) stands out as a viable alternative, providing greater security and performance comparable to classic centralized learning approaches. However, there are still various attacks targeting the local parameters or gradients updated by the participants. Therefore, we present our architecture based on the conjunction of Zero-Knowledge Proof, FL, and blockchain that implements also the decentralized identifier standard. The adoption of this architecture can grant the execution, management, supervision, and updating of the FL process, guaranteeing the resilience of the system and the reliability and traceability of exchanged data. In order to test the performance, robustness, and implementation costs of the proposed architecture, we develop a case study on the prediction of blood glucose levels in people with Type-1-diabetes. The results of our analysis show an improved system in terms of balance between performance privacy and security, guaranteeing high levels of verifiability, therefore proving the proposed architecture suitable for most of the FL processes needed in the healthcare field.

Acetone Sensors Based on Al-Coated and Ni-Doped Copper Oxide Nanocrystalline Thin Films.

Acetone detection is of significant importance in various industries, from cosmetics to pharmaceuticals, bioengineering, and paints. Sensor manufacturing involves the use of different semiconductor materials as well as different metals for doping and functionalization, allowing them to achieve advanced or unique properties in different sensor applications. In the healthcare field, these sensors play a crucial role in the non-invasive diagnosis of various diseases, offering a potential way to monitor metabolic conditions by analyzing respiration. This article presents the synthesis method, using chemical solutions and rapid thermal annealing technology, to obtain Al-functionalized and Ni-doped copper oxide (Al/CuO:Ni) nanostructured thin films for biosensors. The nanocrystalline thin films are subjected to a thorough characterization, with examination of the morphological properties by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD) analysis. The results reveal notable changes in the surface morphology and structure following different treatments, providing insight into the mechanism of function and selectivity of these nanostructures for gases and volatile compounds. The study highlights the high selectivity of developed Al/CuO:Ni nanostructures towards acetone vapors at different concentrations from 1 ppm to 1000 ppm. Gas sensitivity is evaluated over a range of operating temperatures, indicating optimum performance at 300 °C and 350 °C with the maximum sensor signal (S) response obtained being 45% and 50%, respectively, to 50 ppm gas concentration. This work shows the high potential of developed technology for obtaining Al/CuO:Ni nanostructured thin films as next-generation materials for improving the sensitivity and selectivity of acetone sensors for practical applications as breath detectors in biomedical diagnostics, in particular for diabetes monitoring. It also emphasizes the importance of these sensors in ensuring industrial safety by preventing adverse health and environmental effects of exposure to acetone.

E-Skin and Its Advanced Applications in Ubiquitous Health Monitoring.

E-skin is a bionic device with flexible and intelligent sensing ability that can mimic the touch, temperature, pressure, and other sensing functions of human skin. Because of its flexibility, breathability, biocompatibility, and other characteristics, it is widely used in health management, personalized medicine, disease prevention, and other pan-health fields. With the proposal of new sensing principles, the development of advanced functional materials, the development of microfabrication technology, and the integration of artificial intelligence and algorithms, e-skin has developed rapidly. This paper focuses on the characteristics, fundamentals, new principles, key technologies, and their specific applications in health management, exercise monitoring, emotion and heart monitoring, etc. that advanced e-skin needs to have in the healthcare field. In addition, its significance in infant and child care, elderly care, and assistive devices for the disabled is analyzed. Finally, the current challenges and future directions of the field are discussed. It is expected that this review will generate great interest and inspiration for the development and improvement of novel e-skins and advanced health monitoring systems.

The utilization of artificial intelligence in enhancing 3D/4D ultrasound analysis of fetal facial profiles.

Artificial intelligence has emerged as a transformative technology in the field of healthcare, offering significant advancements in various medical disciplines, including obstetrics. The integration of artificial intelligence into 3D/4D ultrasound analysis of fetal facial profiles presents numerous benefits. By leveraging machine learning and deep learning algorithms, AI can assist in the accurate and efficient interpretation of complex 3D/4D ultrasound data, enabling healthcare providers to make more informed decisions and deliver better prenatal care. One such innovation that has significantly improved the analysis of fetal facial profiles is the integration of artificial intelligence (AI) in 3D/4D ultrasound imaging. In conclusion, the integration of artificial intelligence in the analysis of 3D/4D ultrasound data for fetal facial profiles offers numerous benefits, including improved accuracy, consistency, and efficiency in prenatal diagnosis and care.

ISETECH-AI-Powered Dental Consultation Chatbot: Enhancing Public Healthcare Accessibility and Awareness

The evolving landscape of healthcare has witnessed remarkable transformations owing to the integration of Artificial Intelligence (AI) and chatbot technology. This paper presents the development of an AI-powered dental consultation chatbot aimed at revolutionizing dental healthcare accessibility and awareness, particularly in the context of Malaysia. The chatbot serves as a valuable resource for dental self-diagnosis, information dissemination, and remote consultations, reducing the need for in-person visits to dental clinics, especially considering the COVID-19 pandemic. The adoption of chatbots in healthcare has gained momentum, driven by their ability to engage in natural language conversations, disseminate reliable information, and provide personalized assistance. Chatbots have emerged as essential tools in public health management, offering services ranging from disease understanding and information provision to counselling and facilitating doctor-patient communication. In the specific domain of dental healthcare, chatbots have the potential to alleviate challenges associated with iatrophobia (fear of disclosing dental problems) and the need for professional dental consultations. The objective of this work is to develop a user-friendly, AI-powered dental consultation chatbot accessible online. This chatbot allows users to engage in real-life conversations with the machine, conducting short self-diagnosis of their oral health conditions. Users can seek information about their dental issues, understand symptoms, and gain insights into their dental health before or after visiting a dentist. Furthermore, the chatbot system presented in this study holds the potential to ease the workload of dental practitioners by offering an alternative method of consultation. Modern digital dental technologies are increasingly focused on diagnosis and classification tasks, but few have explored the realm of dental consultation chatbots for public use. This project bridges this gap and extends the utility of AI-powered chatbots to the field of dental healthcare. This AI-powered dental consultation chatbot not only addresses the evolving healthcare needs of the post-pandemic era but also contributes to advancing dental healthcare accessibility and awareness among the population.

Facilitators of and barriers to recognition of prior learning in higher vocational and professional education

ABSTRACT Recognition of prior learning (RPL) has gained importance as an instrument for promoting the permeability of education and training systems. Despite the many advantages at the individual and social level, such as supporting lifelong learning processes, previous research indicates that RPL implementation encounters various barriers at different levels. This study aimed to investigate the facilitators of and barriers to RPL implementation for access to higher vocational and professional tertiary education in Switzerland from the perspective of RPL providers. National and organisational framework conditions as well as factors at the study programme level were considered. Data were collected via a nationwide online survey covering professional education institutions. The sample comprised 156 persons responsible for 191 study programmes at professional education institutions in the fields of engineering, economics, healthcare, and social care. A logistic regression analysis revealed that RPL implementation for access to a study programme was most likely in the following contexts: less regulated professional fields, high workforce shortage, and favourable organisational RPL policies. Furthermore, the results suggest that the attitudes towards RPL held by those responsible are decisive in designing effective interventions.

Peer-to-Peer Human Milk Sharing Through Social Media Groups: The Experiences of Women Donors and Recipients in Hong Kong.

To explore the lived experience of donors and recipients involved in peer-to-peer human milk donation. An exploratory descriptive qualitative study. Semi-structured interviews were conducted between November 2022 and April 2023. A total of 50 women consisting of 34 donors and 16 recipients were recruited using purposive sampling from specific social media groups in Hong Kong that focus on breast milk donation and sharing. Data were analysed based on thematic analysis. Five key themes were identified in this study: Motivation; Milk sharing as a social act; Not a Norm; Recommendation to formal milk banks; and Formal donation as the preferred route. The findings indicate informal milk sharing is seen as an inferior alternative to formal milk donation systems. The establishment of formal milk banks is viewed as a way to address the shortcomings of informal sharing. The findings have implications for the healthcare field, particularly in improving the experiences of those involved in informal milk sharing. The insights gained from the experiences of informal milk sharers can directly inform and enhance the services provided by formal milk banks. Given the growing prevalence of online informal milk sharing, healthcare professionals should enhance their understanding of this practice. However, the practice has remained understudied, particularly in Asian region. This study brings together the experiences of both milk donors and recipients, providing a comprehensive view on the phenomenon. The Standards for Reporting Qualitative Research (SRQR). No patient or public contribution.

Nursing students’ perception and attitudes toward utilization of artificial intelligence in health care

The study aimed to evaluate the nursing students’ perception and attitudes toward utilization of Artificial Intelligence in Health care. Descriptive research design. The study was conducted at Vision Medical College, Jeddah. KSA. Two hundred and six students registered in the nursing program were included in the study by using convenience sampling. Three tools were used for data collection, Self-Administered questionnaire, Covered student nurses' demographic characteristics, Perception toward Artificial Intelligence Questionnaire and Students’ attitudes towards Artificial Intelligence Questionnaire. The majority of participants responded that they are familiar with Artificial Intelligence 79.6%, there was a significant positive correlation between student nurse’ demographic characteristics (Living Location, education, Self-Assessment of Technological Competence and Familiarity with Artificial Intelligence) and their attitudes toward using the artificial intelligence. The mean score of the attitude scale was (M= 3.29, SD = 0.76), while the mean scale score of the perception scale was (M= 3.43, SD = 0.69). Likewise, a significant positive association was observed between perception of students and their attitudes towards artificial intelligence (β = 0.855, p < 0.001). The current study revealed that the students most likely held positive attitudes towards AI utilization and had a favorable perception toward utilization of artificial intelligence in health care. Integration of artificial intelligence technology and their utilization in nursing practice as a partner for improving patient outcomes in the health care field.

The value of personal data in the field of health care in the conditions of informatization

The value of personal data in the field of health care in the conditions of informatization

Türkiye’s Vision for Africa: Humanitarian Diplomacy and Development Cooperation

This article aims to analyze Türkiye’s humanitarian diplomacy and development cooperation strategies toward Africa within a theoretical and conceptual framework, assessing their impact and significance on the continent. The research problem addresses how Türkiye’s unique approach, which integrates historical ties, cultural interactions, and development efforts, distinguishes it from other global and regional actors. The arguments presented highlight that Türkiye’s humanitarian diplomacy strategy, based on mutual benefit and sustainable development goals, has effectively enhanced its positive image and influence in Africa. The hypothesis posits that Türkiye’s humanitarian diplomacy and development cooperation efforts have strengthened its position as a humanitarian actor on the continent and contributed to achieving sustainable development goals. The findings suggest that the country’s activities in healthcare, education, agriculture, and socio-cultural fields have created significant impacts, improving local communities’ living conditions and fostering sustainable development. Furthermore, Türkiye’s distinct approach, unlike traditional Western policies, is characterized by an emphasis on mutual interdependence and cooperation, positioning it as a reliable partner in Africa.

Verifiable attribute-based multi-keyword search scheme with sensitive information hiding for cloud-assisted e-healthcare sharing systems

Cloud-assisted e-healthcare sharing systems (EHSSs) play an increasingly pivotal role in the contemporary healthcare field. By outsourcing electronic medical records (EMRs) to the cloud, hospitals can alleviate local storage and management burdens while facilitating data sharing. Due to the highly sensitive nature of EMRs, encryption is necessary before storing them on the cloud. Attribute-based keyword search (ABKS) enables the privacy protection of EMRs with efficient search services. However, there remain some limitations in practical application. Firstly, most ABKS schemes only support single keyword queries, resulting in inaccurate results and wastage of computing and bandwidth resources. Secondly, since sensitive information within EMRs is encrypted as a whole, different data users (including internal doctors and external researchers) should have varying access rights to prevent leakage of this sensitive information. Thirdly, incorrect search results could lead to misdiagnosis or endanger patients' lives and affect researchers' decision-making processes. To effectively tackle these challenges, this paper proposes a verifiable attribute-based multi-keyword search scheme with sensitive information hiding (VABMKS-SIH) for cloud-assisted EHSSs, where we present a secure model for multi-keyword search with two-level access structure by incorporating an improved blindness filtering technique into ciphertext-policy attribute-based encryption (CP-ABE) within existing keyword search framework. Our scheme employs a super-increasing sequence to aggregate multiple filtered data blocks into one unified ciphertext, thereby greatly reducing communication overhead during the transmission phases of ciphertext. To check the correctness of returned results, we introduce a lightweight algebraic signature algorithm based on fundamental algebraic operations. A security analysis demonstrates that VABMKS-SIH is provably secure under the random oracle mode. Additionally, we also evaluate the proposed scheme's performance to demonstrate its utility in cloud-assisted EHSSs.

Doctor interaction behavior, patient participation in value co-creation and patient satisfaction: cross-sectional survey in a tertiary-level hospital from Guangzhou, China

Value co-creation can be defined as the joint benefit that is created by patients and medical service providers through the integration of their respective resources. Participation and interaction between doctors and patients can generate an outcome that benefits both sides. Relevant studies of value co-creation in the healthcare field are limited. This study established hypotheses to explore the association between doctor interaction behavior, patient participation in value co-creation, and patient satisfaction. A cross-sectional survey was conducted with 637 patients (outpatients and inpatients) at a tertiary-level hospital in Guangzhou, China. The analysis result indicated that doctor interaction behavior could stimulate patient participation in value co-creation then increase patient satisfaction. The standardized total effect, direct effect, and indirect effect were 0.641 (95%CI: 0.055 ~ 0.067), 0.546 (95%CI: 0.044 ~ 0.059), and 0.095 (95%CI: 0.032 ~ 0.166), respectively. Patient participation in value co-creation mediated the relationship between doctor interaction behavior and patient satisfaction. Among the different dimensions of doctor interaction behavior, access, risk assessment and transparency dimensions were associated with positive patient participation in value co-creation. During the treatment process, doctor interaction and patient participation can get satisfying results.

Promoting Patient Care Ownership in Medical Students through Standardized Simulations

Background:Professional identity formation (PIF) has been demonstrated to play an important role in medical students' transition from a peripheral role into becoming a physician that bears primary responsibility for the care of their patients1,2. Multiple factors influence this development, including clinical experiences as well as sociocultural, familial, academic, moral, religious, and gender-based roles, values, beliefs, and obligations. Many of these factors can be emulated in simulations to encourage this merging of identities; the most critical of which is patient care ownership (PCO)6. This study was conducted on the 4th year medical students at the Medical College of Georgia (MCG) and compares a self-reported PCO index score amongst the various subsets. These include main vs regional campus assignment for clinical years, gender identity, ethnicity, and prior healthcare experience before medical school. Methods:For this study, we distributed a questionnaire developed by Avey et al. (2009) and adapted by Wyatt et al.11 (2023) to evaluate the feelings and opinions of medical students regarding their level of patient care ownership11,16,17. To evaluate the effects of the longitudinal simulation curriculum on PCO as measured with the survey instrument index scores, independent sample t-tests and ANOVA between different groups with a Tukey HSD multiple-comparison post-hoc were done. Groups analyzed for differences included campus, gender identity, race, ethnicity, and prior experience in the healthcare field. Results: A total of 201 students were invited to participate in this study. There was no statistically significant difference between students from main and regional campuses in PCO index scores. When comparing between gender identities of male, female, and nonbinary, there was no significant difference (p = 0.248). Comparing ethnicities did not show any significant differences (p = 0.936). Finally, there was no significant difference in PCO index scores between students in regard to their prior healthcare experiences (p = 0.155). Conclusion:This study highlights the importance of a comprehensive and intentionally-designed simulation curriculum to prompt PCO in order to facilitate PIF prior to residency, for the purpose of producing more confident and competent intern physicians2. Conflict of interest: none Treatment of human and animal subjects: not applicable

From living heritage values to value-based policymaking: exploring new indicators for Abu Dhabi’s sustainable development

This exploratory study offers an analysis of Abu Dhabi’s living heritage ecosystem. It borrows from approaches predominantly used in the healthcare field and develops a value-based framework that provides a broader understanding of heritage and its integration into innovation, economic development, and societal change in Abu Dhabi. This framework includes three steps: (1) identification of the individual heritage values (‘heritage drivers’) through ethnographic interviews; (2) aggregation of the dynamics impacting the practical realization of these values and relative identification of value-based policymaking areas; and (3) suggestion of an approach that builds indicators to assess the impact of living heritage policies contributing to the achievement of the UN SDGs and their relevance to grassroots ‘heritage drivers’. This study also addresses the existing methodological gap in qualitative surveys focusing on Abu Dhabi’s cultural landscape, which is characterized by ongoing transformations. The UAE’s current social, economic, and political conditions continuously shape Abu Dhabi’s approach to using its rich tangible and intangible heritage as a catalyst for new articulations between the ‘local’ and the ‘global’. Even if the epistemological approach suggested in this paper is far from exhaustive, the interdisciplinary aspect of this research has practical implications that may be useful for international researchers and policymakers working in a variety of fields.

Nursing students and clinical nurses’ perception of research and the role of the clinical research nurse

Background: The nursing profession has made great strides during the last century. Roles such as the clinical research nurse (CRN) have been developed and research is widely recognised as a vital component of the healthcare field. Hospitals involved in clinical nursing research can improve patient outcomes, increase staff and patient satisfaction and reduce hospital costs. Aim: The aim of this literature review is to analyse nurses and nursing students’ perception towards research, with focus on the available career progression for nurses in research. Method: A comprehensive search of literature on ASSIA, CINAHIL, PubMed and Web of Science electronic databases was conducted to obtain relevant articles related to the perception of nurses and nursing students towards research. Results: A total of 31 articles were selected for analysis following review. Three key themes were identified: nurses’ perception towards research; nursing students’ perception towards research; and career progression and job satisfaction. Conclusions: Involvement in research has helped nurses integrate evidence-based practice into their work. This has helped empower nurses to make positive change and develop their skills and knowledge. Unfortunately, research is still considered by many to be a ‘doctor’ field, and nurses have various difficulties accessing funding and education. Research education is vital to building the basic principles of nursing research and creating a positive attitude and perception around research. A change in culture is required to standardise research into university education, increase students’ perceptions and confidence in research, and help nurses incorporate valid evidence-based recommendations into practice.