Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work. Modern technology is driven by Machine Learning (ML) and Artificial Intelligence (AI) based systems. Intensive research and study are being carried out in various domains of AI to integrate them in one way or another in almost all fields of computer applications. The Healthcare sector has also seen rapid growth and technological advancement, especially over the last decade. Numerous attempts have already been made to integrate AI in this sector to ease the lives of both patients and doctors. These applications are AI for drug discovery, diagnosis, intelligent clinical trials, model sharing, and patient care, including maternal care and healthcare robotics . AI is aWhile AI has been a booming and blooming field in healthcare for a few years now, the recent advancements in blockchain-related systems have opened up new doors for decentralized environments. As the COVID-19 outbreak unfolded, people realized the importance of real-time information. The traditional patient-hospital-based healthcare system is falling out of place and the adoption of mobile healthcare has never been faster, especially with the introduction of technologies such as the Internet of Medical Things (IoMT), where smart devices can be used to track and monitor various health conditions remotely . While tBlockchain, originally developed to support the cryptocurrency ecosystem, has recently been used in various other fields to achieve extraordinary levels of security . SimilarThis survey reviews the work of 120 papers taken from reputed journals and publications. Research and review papers based on blockchain, AI, and healthcare technologies were referenced for this survey. Papers on sub-domains of AI, such as those related to machine learning and deep learning and their applications in healthcare, were also considered. This survey also reviews documents discussing the integration of AI and blockchain-based systems. This survey thoroughly reviews the contributions of AI and blockchain towards the healthcare sector and what possibilities their conjunction has to offer in the near future. This survey has a primary focus to allow readers to understand what methodologies and algorithms related to AI and blockchain are used in the healthcare domain.It presents a structured representation of how artificial intelligence could help healthcare professionals in the diagnosis of diseases such as COVID and MERS through the latest algorithms and architectures, in handling Electronic Health Records (EHRs) smartly over computer networks aiding in the research of high-quality, and in monitoring subjects in drug testing and clinical trials to evaluate how a particular drug is affecting humans. It also shows that blockchain could be used to keep a record of the origin and usage of EHRs and helps in keeping the privacy of the clients whose data are being used for medical research. It also extensively discusses the existing challenges of AI and blockchain technologies such as issues with the security of blockchain ledgers, and storage capacities and how creating standard protocols still remains majorly unexplored.A brief comparison with existing surveys and review works is given in This survey is based on more than 100 research and review works. The work utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to select the papers used in this survey systematically. The following sections provide the details about the paper selection process.The paper aims to explain the usage of both AI- and Blockchain-based systems in the healthcare sector. It also tries to explain how the two can be merged to achieve better applications and results. The review includes all the papers closely related to the usage of AI, Machine Learning, Deep Learning, and Blockchain in the healthcare domain.To find the relevant papers, databases such as IEEE Xplore, PubMed, ScienceDirect, Google Scholar, Web of Science, Directory of Open Access Journal (DOAJ), and Researchgate were searched from January 2012 to February 2022. The keywords selected to search over the database were Blockchain, Consensus Mechanism, Smart Contracts, Artificial Intelligence, Machine Learning, Deep Learning, Neural Network, Public Health, Smart Healthcare, Advanced Healthcare, etc.Articles published after January 2012 were taken into consideration for this review, and the remaining were excluded. This is because the work carried out in the blockchain domain before this period is not substantial and a wide range of applications of blockchain, including that in healthcare, started after cryptocurrencies became popular. The abstracts of the shortlisted papers were then evaluated, and only documents mentioning AI or machine learning in healthcare, AI-powered Blockchain, Blockchain in healthcare, or smart healthcare were considered for the review. As a result, this paper offers a systematic review of chosen research articles, recent review papers, technical notes, and other materials relating to recent breakthroughs in AI-powered blockchain for healthcare, structured systematically.Articles that were not written in English were initially rejected. This was carried out to avoid any discrepancy that might come from the authors’ lack of understanding of other languages. The duplicate papers were then discarded, as were those unrelated to the study based on their title. This review did not include any articles or papers published before 2012. This review did not include case reports, case series, letters to the editor, opinions, editorials, correspondence, short communications, etc.Initially, 2127 papers were collected for this review in the initial stage after searching based on the above-mentioned keywords. After removing duplicates and articles not in the English language, or those not found relevant based on the title, 723 papers remained. The abstract and full text were checked in some cases to analyze their relevance based on the topic of discussion, after which 120 papers were selected for the final review, as shown in The survey is organized into six sections. Blockchain technology is the foundation of modern cryptocurrencies, and they are called so because of their heavy use of cryptographic functions . SatoshiIn most modern applications and services, the entire system’s control lies in the hands of a centralized authority that manages and takes decisions regarding the data being handled . A blockTo achieve its main goal of decentralization, blockchain uses a P2P network that maintains a distributed ledger that is mutually agreed upon . Each syThe network layer ensures that the nodes can find and interact with each other. The goal behind this is such that the workload can be shared, and the control is distributed . A node Consensus layer is essential for the blockchain system to exist. This defines how a block gets validated in the network: the process and the requirements . ConsensDifferent types of consensus protocols have been proposed and all of them differ in their underlying principles and applications. Some of the mainstream consensus mechanisms that are widely used are Proof of Work (PoW), Proof of Stake (PoS), Prime Number Proof of Work (Prime Number PoW), Delayed Proof of Work (Delayed PoW), Delegated Proof of Stake (DPoS), and Proof of Activity (PoA) ,22. ConsThe Data Layer is where the blockchain network’s actual data are stored and maintained. Various cryptographic approaches are used to secure the security and integrity of the recorded transactions. The cryptographic hash of each block connects it to the previous block. In the case of Bitcoin, the hashing function is SHA256 , while iOne block can contain multiple transaction information in a blockchain. Each block in the blockchain, along with the hash, contains a timestamp, nonce, and the root of the Merkle tree containing information about all the transactions included in that block. Merkle Tree is a tree-like data structure in which the leaf nodes contain the hash of the parent node. To know more about Merkle trees, readers can refer to . The traWhen machines perform ordinary tasks associated with human intelligence, it is referred to as Artificial Intelligence. The idea of AI was first proposed by Alan Turing in 1950. A sub-division of AI is Machine Learning (ML), where computers learn and draw conclusions from data without being explicitly programmed to do so. As advancements have been made in ML and Deep Learning (DL) systems, they have provided improved performance and unlocked new possibilities in various domains—computer vision, text and image analysis, speech processing and recognition, etc. . NowadayML/DL models have hugely improved healthcare epidemiology through their improved disease prediction and identification models. Tasks such as predicting the time before septic shock onset , body orIn the past, patients suffered from different diseases that, in the end, were mostly treated with the same drugs and the physicians could not understand why some medicines worked for some patients and not for others . Now, wiNot only diagnosis but also procedures such as robotic surgeries for treatment are made possible using AI-based systems. Nowadays, many healthcare organizations use remote assistance services using systems such as a chatbot that takes in some questions from the patients and provides immediate responses. This is especially prominent in the mental health assistance sector. In addition, these bots can also provide medical information and answer related queries, and the underlying mechanism behind these bots is Natural Language Processing (NLP) .As advancements have been made in the field of AI and Blockchain, efforts have been made to combine the features and powers of both to create a secure and intelligent system that contains the privacy benefits of a blockchain and the predictive accuracy of AI. To improve the impact on the medical domain, adding on multiple aspects such as analytics, diagnosis, medical report validation, and decision-making, decentralized AI can be combined with blockchain . This section illustrates the various methods and techniques that have been used thus far to combine AI and Blockchain for healthcare applications. The section is further divided into Machine Learning based methods and Deep Learning based methods which discuss some of the state-of-the-art machine and deep learning algorithms, and the blockchain platforms and types that have been used to contribute towards healthcare.An artificial neural network (ANN) is a machine learning model motivated by the neural networks of living objects, especially humans. Blockchain technology can be applied to train neural networks, particularly in choosing the best possible configuration of weight for training or over-training. This is based on the blockchain activity paradigms and is used to overcome the time-consumption process . Heavy cNaive Bayes is a family of algorithms, based on Bayes’ theorem, wherein each feature being classified is independent of the other. It has been used to implement decentralized and collaborative AI on blockchain  that canThe decision tree algorithm belongs to the domain of supervised learning techniques. They are used for solving both classification and regression problems. It creates a set of decision rules  which arA medical diagnosis can become fairly expensive under certain conditions, and it also becomes essential to deal with the cost implications. Freitas et al.  has put The K-Nearest Neighbor algorithm is one of the most straightforward machine learning techniques that belong to the supervised learning category. In this method, the new input is given to the algorithm and is compared to the available cases. Based on this comparison, it is assigned the target value that occurs the maximum number of times in that group . KNN hasK-Means Clustering is an unsupervised learning algorithm that has the ability to group the unlabeled dataset into clusters. “K” simply fixes the number of clusters to be formed. Each cluster is associated with a centroid, and each point’s distance from the centroid should be minimized. Through multiple iterations, this distance is reduced, and each data point is associated with its most suitable cluster out of the ‘K’ clusters. A working model has been developed that utilized coupled integration of K-means clustering and Naive Bayes techniques where, first, the identified attributes were passed through the K-means model and were assigned a particular cluster, which in turn was analyzed by the Naive Bayes instance . Figure The Random Forest Algorithm  is a supAnother important aspect of healthcare management is tracking, controlling, and limiting the spread of infections in intensive care units. Garcia et al.  discuss The Support Vector Machine algorithm  is also The use of rough set theory and support vector machines in creating an improved technique that helps deal with the redundancy of the dataset is proposed . The expDeep learning has received a lot of attention in the last few years because it has a lot of promise in terms of making good decisions. Today’s deep learning architecture is built mainly on centralized servers, which is inefficient in terms of operational transparency, threat prevention, and trustworthiness, and is heavily reliant on statistical analysis. Deep learning technology is extensively used today in several industrial applications. Blockchain technology and deep learning, when put together, can provide massive leaps in the trustability and accountability of the systems in which they are being incorporated. Deep neural networks are provided with large volumes of information with a range of cases, which they use to learn features and generate probability vectors as an outcome. Even though deep learning models perform amazingly well on raw data, standardized data still matter in scenarios where real-life problems are dealt with. The blockchain is a worldwide database that is decentralized and verifiable, allowing all nodes in the network to store and trade data.Recurrent neural networks are specialized versions of neural networks that can remember the input that is given to them. This ability possessed by the recurrent neural networks makes them very effective in problems where sequential data are being dealt with. For this reason, they are also called “hierarchical learning” . Mantey A deep autoencoder is made up of two symmetrical deep-belief networks, one with 4 to 5 shallow layers that is responsible for encoding and the other with 4 or 5 shallow layers for the decoding task. They function by reducing the input into a smaller representation and recreating the output in a similar form as the input. They are deep neural networks that are used to replicate the values at the output layer with the same number of neurons in the output layer. Training datasets with a Deep Boltzmann machine rather than a Restricted Boltzmann machine yields superior results in terms of unsupervised learning tasks .A deep belief network (DBN) is a generative graphical model, a form of deep neural network, made up of many layers of hidden nodes connected by links between levels but not between nodes at the same level. Even though the DBNs have been phased out of use today, since they are generative networks, they still have a great capacity for dealing with both unsupervised and supervised learning problems. Movahedi et al.  discuss Convolutional neural networks (CNN) are specialized deep neural networks that deal with image recognition and image processing. The inputs that are served to the CNNs are images. The images are usually made up of thousands of pixels that have values in several channels. The unique feature of the CNN architecture is the utilization of convolution, which is an image processing technique that helps in reducing the weight count to a manageable number, which results in faster processing of the images. Yadav et al.  have disThe COVID-19 outbreak has affected the lives of people throughout the world. Gaur et al.  demonstrA Generative Model is a form of unsupervised learning approach that aids in the comprehension of many sorts of data spreads, and it has acquired a lot of attention in recent years. To produce new points with some variability, all generative models attempt to learn the true data distribution of the training set. DGMs are capable of approximating multidimensional probability distributions . GeneratDRL, or deep reinforcement learning, is a rapidly growing field that combines reinforcement learning and deep learning. It is one of the most extensively utilized and popular machine learning algorithms as it can handle a wide range of previously unattainable complicated decision-making tasks. AI-powered blockchain can have a huge number of applications in healthcare. As a shift is happening toward the usage of smart devices for patient health monitoring and tracking, a large amount of data is produced which can be leveraged by various AI-based systems for decision-making . As healSimilarly, Blockchain also provides various ways to improve the healthcare system. It finds its application in patient data management, pharmaceutical research , managinWhile both Blockchain and AI have helped the healthcare domain in more than one way, they still have some issues when used on their own ,81,82,83Electronic Health Records (EHRs) are longitudinal data collected in electronic format during the routine delivery of healthcare. Maintaining EHR provides a lot of insight and opportunity to enhance patient care, improve performance measures in clinical trials and enhance the identification of probable diseases in a population in the near future. It also helps in analyzing whether new research and discoveries in healthcare are providing any improved outcomes or not.EHRs need to be kept secure as they can contain any personally identifiable information (PII) of the patient that, if exposed, can lead to a breach of data privacy . Many ofAnother important contribution that blockchain makes towards maintaining EHRs is interoperability . PatientEHRs are also very useful when used with AI systems. Building upon traditional Early Warning Systems (EWS), which aim to predict acute critical illness, data from EHRs play a crucial role in improving the performance and accuracy of predicting acute critical illness . MethodsAI can be used to analyze medical records and offer information to physicians. Based on past data and family history, algorithms can use EHR to forecast the possibility of a disease. To train AI systems, large volumes of data are used, and the algorithm develops a set of rules that relates its observations to the final diagnosis along the process. When AI is provided with fresh patient data in the future, it may make a judgment about the patient based on prior experience . AI techCustomers have easy access to copies of their electronic medical records, which may then be licensed to other medical institutions or third-party platforms. The doctor must upload and adjust permissions based on client consent because the customer has read-only access. Doctor management contributes to the client-centered paradigm of the system. Blockchain technology can allow for the interoperability of patient and medical staff identification assurance and verification. A verification platform has been created using blockchain that verifies physicians’ identities and certifications regularly, ensuring patient safety and high-quality medical treatment .Telemedicine and remote patient monitoring have been the need of the hour since the COVID-19 pandemic hit. Most of the current telehealth and telemedicine systems are centralized and lack privacy, security, and openness . The conBlockchain can help improve the telehealth system by offering remote health services in a decentralized manner which will make the data tamper-proof and provide the necessary transparency . Mobile Personalized predictive modeling, which focuses on constructing tailored models for specific patients, has proven to be more effective in leveraging a wide range of health data than global models trained on the entire population. Individual features are extracted using data from similar patient groups in personalized prediction models. The time fusion CNN, unlike a regular CNN, can learn both local temporal correlations and contributions from each period. To rank comparable patients, the probability distribution developed as part of the similarity learning process is employed .CNN models can also help in finding frequently related health factors that may influence health. Patients’ medical data are first collected and then preprocessed. To discover relationships between disorders such as obesity, high blood pressure, and diabetes, the most important indicators are picked from the analyzed data. Then, the productive correlation among the components is examined to see which factors are positively and negatively connected. Finally, the needed information is derived by selecting the regular co-occurring factors . Figure Significant work has been carried out in the domain of Bioinformatics under Artificial Intelligence and Machine Learning. To aid in the prediction of sequence requirements of DNA-protein binding, a high-order convolutional neural network (HOCNN) was devised, which used a high-order encoding strategy to incorporate high-order relationships among nucleotides . It alsoTo overcome one of the difficult tasks in sequence analysis, which is to identify protein remote homology, a Long-Short Term Memory (LSTM) based predictor was proposed in . The DL-Targeted drug delivery has seen numerous advancements over the last decade, including the emergence of implantable microchips for the controlled delivery of drugs . These sHe et al.  listed fAlong with ML techniques for accurate and efficient drug delivery, pharmaceutical companies can leverage blockchain to manage their supply chain and ensure drug safety. Abbas et al.  proposedClinical trial management plays a critical role in developing and validating medicines and drugs for their usage and consumption in the whole world. Clinical trials are carried out in two phases: patient selection and patient monitoring. For patients who desire to engage in a clinical trial, there are certain eligibility, appropriateness, motivation, and empowerment requirements. The medical history of a patient may preclude them from seeking treatment. Patients who are eligible for testing may not be in the proper stage of disease or belong to a certain sub-phenotype targeted by the drug being tested. Blockchain offers a damage resistant and decentralized data storage for storing medical information of the patients . Unsupervised machine learning can be used to uncover patterns in clinical variables that can be used to identify patient phenotypes that will benefit from the intended therapy or intervention. Because unstructured data are so vital for phenotyping and forming representative cohorts, incorporating more patient data is a crucial step toward generating good, representative cohorts . Using DL algorithms such as NLP and Optical Character Recognition can help in identifying patient cohorts. AI and machine learning technology may also be used to predict a patient’s risk of dropping out dynamically or to detect the onset of patient behavior that could suggest a problem in a phased manner . BlockchA comprehensive overview of techniques and uses of CNNs and their variants in medical image interpretation, including the detection of the most recent global pandemic, is important. The authors leverage their own and others’ experiences with CNN applications to provide insight into many state-of-the-art CNN models, issues in building CNN mode, and motivation for medical image comprehension academics and practitioners to employ CNN more broadly .AI can spot indicators of disease more precisely and quickly with the use of medical imagery such as CT scans, MRIs, X-rays, and ultrasounds. It benefits patients by allowing a more accurate diagnosis of illness and more precise therapy options. Deep learning has greatly improved the detection of skin cancers and legions by applying CNN architecture for identification .In an automated approach for micro-organ segmentation, there are two processes: localization and segmentation, which are especially important when there is little training data. The localization stage extracts the region of interest after projecting photos to a common space using a graph-based multi-group image registration technique. In the segmentation stage, a voxel-wise label map is acquired using a trained convolutional neural network. Deep learning is widely used to perform sequential and independent cell identification and classification.A single architecture is used to identify the locations of cells and classify the detected cells concurrently in the proposed synchronized deep auto-encoder network for simultaneous detection and identification of cells in bone marrow histology photographs .The application of deep learning also extends to the domain of drug discovery and manufacturing. Using fully integrated DNNs to develop models is a simple way forward when compounds are presented with a uniform amount of cell definitions . A huge The recent COVID-19 break has presented itself as a big challenge to our balanced world and societies. In situations such as these, deep learning techniques can prove to be extremely useful and relatively quick. Marzouk et al.  showed aMedical insurance is a crucial agreement for any patient, the integrity of which needs to be maintained properly. In a typical insurance system, three parties are involved: the patient, the hospital, and the insurance company. Managing the expenditure records can sometimes become difficult, and it also lacks transparency. Insurance is also prone to fraud, which can be very hard to track. Blockchain and AI can help mitigate these risks.Yuliang et al. proposed a fraud-resilient medical insurance claim system based on HFDA . Zhou etDeep learning is a data-modeling approach that uses many processing layers to reflect a high level of abstraction. DL approaches have progressed to the point that they are now routinely employed by researchers all around the world to identify a variety of illnesses. DL has shown exceptional performance in a number of applications by recognizing long-range correlations and effectively creating dense hierarchical order features. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and other deep neural networks are used in deep learning (RNN).A study was presented that evaluated a CNN-based approach for predicting diabetic retinopathy prognosis . They adThe authors also looked at research that used pediatric EHR data to offer a way for applying an RNN-based predictive diagnostic algorithm. This work used NLP concepts to generate a feature vector for a collection of Chinese unstructured EHRs and then moved the values into a sentence vector. The suggested bidirectional Recurrent Neural Network was used to identify the patient’s symptoms and their association. A dataset of 81,476 pediatric records was utilized to train as well as test the model which had an accuracy of 80.912.The use of blockchain and artificial intelligence technology in the field of healthcare has revolutionized many sectors, but all the advancements come with a cost. The new technology and implementations demand extremely high operation costs, lack of flexibility, and huge amounts of data storage on the local systems. In certain situations, due to the absence of an arbitrator, the legal threats of blockchain cannot be dealt with. Healthcare databases consist of personal and confidential information of the patients, and hence preserving their privacy is of utmost importance. There are quite a lot of privacy issues linked to sharing of data in public blockchains. The decentralized and transparent property of blockchain poses major threats to the data. Blockchain connects various members of an organization and, in this scenario, it can be difficult to identify the subject responsible for the illegal use of the data. The scenario can get worse when parties outside of the healthcare network are added to the blockchain. Various protocols such as distributed consensus have been introduced to overcome the privacy issues interlinked with blockchain technology The requirements for running a successful blockchain and the requirements for running Artificial Intelligence on a system are quite different and require a good amount of data storage and capacity in a system. Current blockchain technology also comes with a limitation on the number of transactions that can be performed per second in the network. This also comes with the fact that heavy computation power is required to handle the transactions on a scale. This also leads to high energy consumption which has been a lingering concern with blockchain technology. The process of consensus and other protocols lead to a great amount of energy consumption which can hinder the speed and efficiency of the process .Along with that, the recent advancements in the field of big data analysis have increased the need for data storage since every project requires a good amount of data to be analyzed to reach the conclusion or obtain a definitive output with higher prediction power. The provision for some of the greatest big data analysis tools is required, but it would cost a lot more than usual and there will be certain drawbacks of these tools as well . AI-based systems also run on accelerated hardware. Most of the AI architecture involves complex calculations, with many algorithms requiring processing a huge amount of data in each cycle, which causes latency. Although faster networks can improve the latency, many algorithms need to wait for a complete cycle before processing the next set of data.Recent research projects have emphasized some of the inherent security weaknesses of blockchain technology. There have not been a lot of recent advancements in the blockchain protocols and hence it is easier for the attacker to analyze and determine the loopholes in the existing system. There are various threats such as data modification and stealing which are possible in the case of blockchain technology. The malicious attackers also have the provision of injecting false data or sample inputs which can not only affect the AI learning but also completely change the final output of the model, which poses a big threat to the customer’s health and wellness. Blockchain technology has also been found to be susceptible to cyber-attacks such as the DNS (Domain Name System) attack and mempool attacks . Before coming up with the implementation plan for blockchain and AI applications in the healthcare sector, various regulatory laws should be referred to. Most of the integration of AI into healthcare is still limited to academic research, and there are only a few practical applications that have been possible, primarily because of interoperability issues. There also is a question as to how AI diagnostics systems should be placed in the healthcare sector. It can be either placed as an AI as a helping hand to the doctors or AI replacing the doctors. However, both of these methods come with the thought of “dangerous AI” considering the sensitivity of the healthcare domain.One of the biggest challenges in adopting blockchain widely across healthcare sectors is the question of ownership. As private and public blockchain exists, it becomes a valid question as to who owns the data in the case of either of the two blockchain solutions. Sometimes, due to the absence of an authority that can be held responsible for various actions of the technology, there are quite a lot of legal and regulatory law issues that exist . There cThe model consisting of deep learning methods and Blockchain technology requires more detailed research since the field is evolving at a very fast pace. Deep learning can have a great amount of application in detecting myopia and other internal body diseases and using it with blockchain technology provides both accuracy and security to the patient’s details. To collaborate in both fields, it is important to carry out intense research before submitting a design of the proposed model, and this could also come in our future research objectives. These models will also be able to detect chronic diseases such as cancer, diabetes, arthritis, and some major heart diseases with much better accuracy and maintain the security of the data . Figure There is still scope for a lot of improvement in the security and privacy of AI-powered blockchain technology. Implementing the latest hash algorithms, and using a more rigid verification system are some research areas that can be explored in the future. Using two-step and multi-step authentication for every node on the blockchain will have its advantages and disadvantages but cannot be determined for this research interest. Using the traditional consensus protocols such as proof-of-work and proof-of-stake would not be a good idea since these protocols have been in use for approximately the last 10–11 years and various loopholes have been found in them. New consensus protocols such as proof-of-elapsed-time and Delegated Proof of Stake could be applied in the AI-powered blockchain, which will improve the security and efficiency of the system to a great extent.Cooperative computing is considered to reduce the time of computing the hash of a block when compared with a non-cooperative computing strategy . Using aThe paper gives an overview of Artificial intelligence and blockchain integration and how they can be applied in the field of healthcare. Several research projects have been carried out on the application of AI and Blockchain in this field, especially during the COVID-19 pandemic. There have been various suggestions to improve the existing models, and all of these have built a future image for blockchain and AI in healthcare. The market of blockchain is expected to cross approximately $500 million by the year 2022. This paper provides a comprehensive review of 120 research articles on the topic of AI-powered blockchain applications in healthcare. It also provides a clearer picture of how artificial intelligence could improve our abilities to identify and quickly act on the diagnosis of diseases and how blockchain could structurally improve the security of medical records and preserve the privacy of the record owners. Various features of blockchain have been discussed which allow it to provide security to the EHR and other confidential medical records. Artificial Intelligence and machine learning integration usually help in automating the process of data analysis and scanning. The paper has given a brief overview of how the blockchain system works and the different layers present in the design. There are mainly five layers present which include the Application and Presentation Layer, Consensus Layer, Network Layer, Data Layer, and Hardware/Infrastructure Layer. Furthermore, the integration of artificial intelligence has also been the point of focus. Different machine learning algorithms such as decision trees, support vector machines, KNN, K-means clustering, and artificial neural networks have been discussed. These algorithms have been used for years for improving the process of data extraction, data mining, and data analysis. The integration of machine learning and Artificial intelligence into blockchain technology would provide an automated, secure and decentralized system for data storing and analysis of important and confidential medical records. The work explores some of the applications of AI-powered Blockchain in the healthcare domain such as maintaining and managing EHRs, introducing telemedicine and remote health monitoring, bioinformatics and genomics, clinical trial management, drug delivery, medical image diagnosis, drug discovery and manufacturing, outbreak prediction, disaster relief and insurance, and disease diagnosis.There are certain limitations of the existing system despite the various advancements happening in the field of machine learning and blockchain. One of the main functionalities of the blockchain system is the level of security it provides to the data being stored on its system, but there are still various privacy issues that persist in this advanced technology. External party access to the database, decentralized system, and various other features of public blockchain can make the data insecure, and attackers can easily gain access to it. Along with these issues, there are some legal restrictions while using blockchain and artificial intelligence technology on medical databases, and these restrictions can act as a major challenge when developing AI-powered blockchain. The paper has also discussed future research possibilities which have the capability of overcoming these limitations and challenges.


To manage the undesirable effect of anxiety on students, a wide scope of research has been dedicated to determining the triggers of anxiety and pedagogical interferences that can assist students with mitigating anxiety. Mindfulness is a relaxation strategy that has been related to constructive impacts when utilized as a managing technique for stress and anxiety. Originating from the construct of mindfulness, there is a multidimensional conception acknowledged as resilience as one of the notions in the Positive Psychology (PP) literature, which highlights the organizations and individuals’ strengths and self-control to comply with accidental states. Given the eminence of mindfulness and resilience in learning situations, the present study sets forth to examine the role of these constructs in alleviating English as a foreign language (EFL) learners’ anxiety. To meet this objective, 502 Chinese EFL learners took part in this research. They were asked to respond to the three questionnaires, namely resilience, mindfulness, and anxiety. To answer the research question of the study, a linear multiple regression was run and the findings demonstrated that mindfulness and resilience together could significantly predict anxiety. Consistent with the findings, some recommendations are presented concerning the implications of the present research. Some studies have been done so far on the state of students’ influential factors in the procedure of language learning, where anxiety in the language class is an example of such factors . AnxietyAlthough all university learners encounter anxiety in their individual, societal, and educational lives, the degree of anxiety and how they react to or manage it is different. The notion of mindfulness, possessing its origin in the Eastern pensive exercises of Buddhism, manages two simple ideas of recognition and a neutral or unbiased viewpoint in each circumstance . That isIn addition, due to its various kinds, mindfulness has been implemented in a vast range of fields. For instance, it has been presented cross-contextually and organized as an intermediary strategy, a mental condition, an attribute, a response-ability, or a therapeutic strategy . InterfeAmong studies on college undergraduate learners, RQ:To what extent do resilience and mindfulness contribute to the Chinese EFL learners’ anxiety?The notion of anxiety is multi-faceted since people who experience foreign language anxiety are prone to sense anxiety when participating in language learning and/or utilization . AnxietyMoreover, anxiety is an intrinsic mood as well as a social structure, i.e., it is co-formed by intrinsic mental procedures, cognition, and affective moods together with the needs of the conditions and the attendance of other individuals . The majResilience refers to an idea about the study of mind and human growth theory which is labeled as a dynamic course relating positive alteration in highly difficult conditions or the capability of people to be resilient when encountering difficulties and should be adjusted to their environment . Due to Resilience is a route that focuses on the relationship between students and their environment and measures the associations among different dimensions of psychological well-being and educational success . It is tBesides being able to conform to challenging conditions, resilience may be comprehended as the strength to adjust to modifications in conditions . That isMindfulness has origins in long-lasting Eastern spiritual customs, in specific Buddhist philosophy that it shows how to be available in the present and how to let go of an overdependency people have when it comes to a sense of personalized identity and instead focus on an expansive link to a feeling of oneness and incorporation in all matters . MindfulFive hundred and two EFL students from Zhejiang Yuexiu University in Zhejiang Province of China were invited to participate in this study. In the sample, there were 128 male (25.5%) and 374 female (74.5%) students. They are all undergraduate students, 61.95% of which are freshmen, 36.25% are sophomores and the rest are juniors and seniors. They major in E-commerce, International Economy and Trade, International Business, Financial Engineering, Taxation, Investment, Economic Statistics, Chinese Language Education, Chinese Language and Literature, and Portuguese. Among them, there were 288 Financial majors, including students majoring in E-commerce, International Economy and Trade, International Business, Financial Engineering, Taxation, Investment, and Economic Statistics.The resilience scale built by Mindfulness was evaluated with the short type of the Five-Facets Mindfulness Questionnaire FFMQ-SF; . The FFMThe Foreign Language Classroom Anxiety Scale (FLCAS) is planned by For questionnaire survey, Wenjuanxing, an online questionnaire platform was used in this study. This platform is popular in mainland China, and the Chinese version of the questionnaire was used for the sake of data accuracy and easy understanding for the EFL students. Prior to data collection, the students were made well informed of the research purpose and provided their willingness consent to participate in the current study according to the ethical issues of the university research ethics committee. Then we embarked on collecting the research material. The whole period of data collection lasted for 3 weeks at the university.To study the plausible answer to the research question of the study, a linear multiple regression analysis was run after checking its related assumptions. Indeed, multiple linear regression analysis was chosen to answer the research question as there were two predicting and one predicted variables. The analysis shows the going-togetherness of each predicting variables and the predicted variable both separately and jointly. In other words, it shows to what extent anxiety can be predicted by either of resilience and mindfulness and both together. Accordingly, both the correlation coefficient (r) and the regression coefficient (beta) are estimated to answer the research question.The study intends to estimate how resilience and mindfulness contribute to anxiety among Chinese EFL learners. Before conducting any statistical analysis to answer the research question, it is necessary to ensure instruments used to measure our variables are reliable. SD = 12.70). The mindfulness instrument included 24 items and had Cronbach’s Alpha = 0.89, above the required value of 0.70 for reliability. The mean score for resilience is 145.62 (SD = 29.40). The resilience instrument included 40 items and had Cronbach’s Alpha = 0.97. The mean score for anxiety is 97.66 (SD = 19.24). The anxiety instrument included 33 items and had Cronbach’s Alpha = 0.93. Accordingly, all the instruments have Cronbach’s Alpha values >0.70 which ensures the reliability of the instruments.As seen in The objective of the study was to estimate how resilience and mindfulness contribute to anxiety among learners. The statistical analysis for such estimation was done through multiple regression. Anxiety was the dependent variable in the multiple regression model while mindfulness and resilience were the independent variables or predictors. Before performing regression analysis, certain assumptions needed to be met. These assumptions require that variables are continuous, the sample size is large, there is no multicollinearity, there are no outliers, data is normally distributed, relationships between predictors and dependent variables are linear, variances of residuals are the same (homoscedasticity), and residuals are independent . The varAs seen in As seen in Based on the normal probability plot , all theR2 value multiplied by 100 indicates the prediction power of the model , and the ANOVA test shows the significance of the regression model.In the next step, the regression model was consulted to check how well mindfulness and resilience together contribute to anxiety . In the R2 = 0.153) in anxiety which was significant . In other words, mindfulness and resilience together could significantly predict anxiety for 15%.Based on regression output presented in t = 9.14, p < 0.01, and resilience had a Beta value of 0.05 which was less than that of mindfulness but also significant, t = 1.15, p < 0.01.Mindfulness and resilience together could significantly predict anxiety. As seen in The current study tries to inspect how mindfulness and resilience contribute to anxiety in learners. The study included 501 participants whose mindfulness, resilience, and anxiety were measured using highly reliable questionnaires. The data analysis was done using multiple regression and displayed that mindfulness and resilience together could significantly predict anxiety for 15%. Both mindfulness and resilience could uniquely and significantly contribute to anxiety. In this section, it is discussed how theoretical explanations justify the findings and how they contribute to foreign language education. Indeed, the findings promote mindfulness and resilience as tools for dealing with anxiety in foreign language classrooms. Recent studies show that the role of emotion, including anxiety, is gaining more prominence in language learning e.g., . AnxietyThe current study’s findings can also be discussed from an educational perspective. To put it simply, the findings promote mindfulness and resilience as tools for dealing with anxiety in foreign language classrooms. Foreign language learning experts have always suggested that teachers seek ways to diminish anxiety in language classrooms . AnxietyThe outcomes of this research agree with It is contended that improved mindfulness could enable people to encounter less anxiety by acting more congruently in their settings, and thus, be better capable of satisfying their demands and acting consistently with their principles. The results are therefore consistent with previous research carried out by Furthermore, resilience is referred to as tolerability, responsibility, and perseverance . In otheGrounded on the upshots of the study, it is evidenced that mindfulness and resilience are mental powers that can alleviate learners’ anxiety. To decrease students’ language anxiety, both educators and learners must act in some ways. Indeed, the findings in the present research are according to what learners can do to mitigate their anxiety but include recommendations for educators. Because recognizing what to do to decrease tension in the class is a basic problem for many educators, becoming aware of such constructs, namely resilience, and mindfulness could be a reasonable necessity in this domain. Academic organizations and governments might make plans to assist learners with being more resilient, thereby reducing educational isolation and educational anxiety.Indeed, the results of this research highlight the prominence of encouraging mindfulness and resilience in procedures of learning and education. Since an efficient academic system should guarantee the lessening of students’ anxiety during learning and offer learners higher success, being mindful and resilient need to be practiced in academic settings. The significance of resilience in educational settings is because the specific learners who encounter distressing and troubling circumstances become stronger without building any mental syndrome . AccordiIn addition, students may enjoy the integration of mindfulness into the educational setting, because mindfulness exercises can help teachers cope with their stress. Similarly, mindful, and resilient learners tend to have higher engagement and participation in academic activities. Indeed, support exists for designing plans for language students that promote resilience and mindfulness leading to the improvement of their ability to effectively manage the challenges that they face in the process of their learning. Additionally, Mindfulness can assist individuals to better react to mental suffering as opposed to responding in a maladjusted or overindulging way . People Considering the scarcity of studies on the structures of constructive affections and mindfulness, the present research increases the attention to comprehending how mindfulness is employed as an instrument for changing and maintaining constructive experiences. The outcomes of the present study have significant suggestions for those interested in planning interferences with the specific objective of improving mindfulness to reduce students’ anxiety. Particularly, research results can be applied to the theory of broaden-and-build as they recommend that mindfulness generates constructive affections like happiness as well as optimism that results in less stress.By familiarizing students with mindfulness activities, learners can get higher awareness regarding the psychological-physical link that can also provide more mindful well-being options. Such competencies are useful not only to manage the routine stressing factors of university life but are competencies throughout life that can help them during challenging transitions and disasters throughout life in different conditions like the place of work, learning settings, family conditions, and social environments. Higher institutional support needs to be provided for mindfulness classes so that they are highly accessible, conveniently available, and even obligatory for the whole learners. Mindfulness assists the improvement of constructive affections that support the modification of negative affections like apprehension, regret, and anxiety. Such constructive affections may assist in creating individual reservoirs that result in higher life appreciation, and further meaning for life, and help shape a protecting defense to address the future problems that is in direct relation with resilience.Syllabus designers are extremely suggested to integrate content materials that stimulate and encourage mindfulness followed by resilience. Indeed, diverse types of treatments and interventions can be designed to enhance mindfulness and resilience that bring about a reduction in learners’ anxiety. Finally, it should be noted that the current study, like many empirical studies, is not without limitations. In the present study, the researcher relied on surveys and quantification to capture the associations between anxiety, resilience, and mindfulness. Therefore, it lacks a deep description of the connection between anxiety and resilience and between anxiety and mindfulness. It is recommended that future studies include descriptive accounts of the foreign language learners’ status of anxiety, mindfulness, and resilience and explore how individuals seek unique ways to deal with their anxiety and what are the obstacles and challenges to their mindfulness and resilience. The target of the present research was merely English language college learners. Despite the dearth of studies in other areas, more investigation is desired in education to examine the connection among some issues beneficial to learning at various degrees of EFL learning, at private organizations, and even among English educators. Further research is suggested to investigate the conformity among the variables investigated in the present research concerning language success. Furthermore, in the present research, the researcher investigated the relationship among the constructs quantitatively. Both the qualitative and quantitative methods can be used to do a more precise study. Selecting several other methods, especially observation or qualitative approaches, can provide a comprehensive view of the relationship between mindfulness and resilience in EFL learners’ anxiety. Also, a longitudinal design study will be done in further studies to evaluate the long-lasting impacts of mindfulness on mitigating EFL learners’ anxiety. The participants of the study were selected only from one university, which may influence the generality of the research findings, so more research can be carried out in future among different universities and in different settings.The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The studies involving human participants were reviewed and approved by the Zhejiang Yuexiu University Ethics Committee. The patients/participants provided their written informed consent to participate in this study.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The website Sexe Joves is a website on sexuality of the Department of Health of the Government of Catalonia (Spain). This study aims to understand the experiences and opinions of people aged 14 to 25 regarding this website, taking into account sex, gender identity, sexual orientation, socioeconomic status and location within Catalonia . With the objective of improving the website and adpating the resources allocated to it, this study evaluates whether this population is familiar with it and uses it, as well as the website’s usability and accessibility , usefulness and the relevance of its content. A parallel convergent triangulation design is used: a qualitative study using a social constructivist perspective, and an observational, descriptive and cross-sectional quantitative study. We conduct a discourse analysis of participants and use an “ad hoc” questionnaire to collect quantitative data. A descriptive analysis of all variables is carried out. Affective-sexual education aimed at young people must stem from their participation and the whole range of sexual and gender diversity in order to reach the entire population equally. This analysis will contribute to the design of new strategies for the wesbite Sexe Joves, a public health resource, in order to improve affective-sexual education for young people. Health professionals have implemented sex education for adolescents for decades. Oftentimes, such education has not been based on a holistic model of health but focused solely on preventing sexually transmitted infections (STIs) and unplanned pregnancies. This model replicates heteronormative sexuality and drives young people to use mainstream pornography .Affective-sexual education for young people pivots on a positive sexuality, which is understood as something to be enjoyed, not centred around fear, and removed from the coitocentric, biomedical, heteropatriarchal and binary model. This type of sexuality is based on self-esteem and self-care, as well as respect and empathy . It shouGlobal data support the need for this type of research. Violence against women by their intimate partners and others is currently considered a major public health problem . In SpaiStatistics show an upward trend in the diagnosis of STIs, a consequence of risky sexual practices without the use of barrier methods . WorryinIn agreement with the Istanbul Convention , discussIn 2016, the Guttmacher Institute and The Lancet created a commission on Sexual and Reproductive Health and Rights (SRHR). They concluded that the 17 SDGs are insufficient for a broad implementation of SRHRs . In theiIn order to improve these indicators, guidelines and programs on sexual health for young people should be re-evaluated and include the thoughts, experiences and opinions of the target population. The result should be an affective-sexual education that is truly effective.Affective-sexual health education for young people in Catalonia is provided by the public universal primary care system, specifically from the Reproductive and Sexual Healthcare units  ,18. Thiswww.sexejoves.gencat.cat). The project started in 2003 and was fully operative in 2006. It was led by midwives from the outset, with support from the government of Catalonia and the Catalan Institute of Health ). A multidisciplinary team of 65 professionals from various institutions related to sexuality and youth participated in the creation process. This group prepared a document with content for the website, which was validated by a total of 153 young people from secondary schools from across Catalonia [The website Sexe Joves (WSJ) on sexuality is a reference for health professionals in Catalonia  sexual and gender diversity and (2) socioeconomic factors. The results might also provide advice for professionals of primary healthcare, school health and community nursing.To evaluate the WSJ as a source of affective-sexual health information, education and communication for young people.To outline a new participatory model of affective-sexual education for adolescents and young adults led by midwives and/or nurses that incorporates the gender perspective and sexual and socioeconomic diversity  to determine socioeconomic conditions. This is a synthetic index that summarizes in a single value several socioeconomic characteristics of the population by small areas. The index includes data on employment, educational level, immigration and income of all people living in each territorial unit, based on six sectoral indicators ) and thaTo provide a new framework for the WSJ that is competitive and effective with regard to the affective-sexual education of young people.To involve the target population in the creation of this intervention for their emotional and sexual wellbeing.Objective: To analyse experiences and opinions on the WSJ of people aged 14 to 25 from a social constructivist perspective . ParticGeneral objective: To understand how the youth population of Catalonia evaluates the WSJ.To determine what content of the WSJ receives the best rating by young people.To identify whether assessment of content and usability of the WSJ varies according to the following factors: age, sex, gender identity, sexual orientation, culture of origin, socioeconomic status, area of residence  and motivation for consulting the website .To identify whether assessment of content of the WSJ is different in young people that had previously used it.To assess if having previously consulted the ASSIR unit is associated with a different assessment of the WSJ content.To determine if the target population knows about the WSJ, and what percentage use it and consider it useful.To evaluate if socioeconomic conditions determine the use of paper vs. digital questionnaires.To determine if young people in Catalonia visit other affective-sex education websites, and which are the most popular.To determine if young people in Catalonia follow any influencers for affective-sexual education, and which influencers are the most popular.To determine the percentage of young people in Catalonia that visit pornography websites.Specific objectives:We will use a parallel convergent triangulation design  and concThe study will adopt an explanatory/interpretative model within a social constructivist framework. It will aim to describe and interpret social meanings generated collectively, i.e., the experiences and opinions of young people regarding the WSJ, affective-sexual education and their experience and needs regarding their sexuality. Investigators will have to discriminate between the hegemonic and counter-hegemonic discourses that originate from these social constructs ,34.This is an observational, descriptive and cross-sectional quantitative study, for which an “ad hoc” questionnaire has been created.Since this protocol uses mixed methods, how the researchers’ background might influence the analysis must be explained. The principal investigator and most researchers are midwives working at ASSIR units, where they offer affective-sexual education to young people at individual consultations as well as in the community, at education centres and various associations and public spaces.Participants will be teenagers and young adults aged 14 to 25 able to express themselves in one of the two official languages of Catalonia . We will exclude people who do not wish to participate in the investigation and people under 16 who wish to participate but whose parents/legal guardians do not sign the informed consent form. The sampling will be theoretical. Participants will have to meet different profiles defined by representativeness criteria specified by the research team in order to provide heterogeneity to the sample and until reaching theoretical saturation of technical data. The representativeness criteria are age (two groups: 14–18 and 19–25), rural and city areas, different socioeconomic conditions, all sexes , different gender identities  and different sexual orientations .We have devised three methods for participant recruitment: (1) convenience sampling will be used in education centres ; (2) consecutive sampling will be used to recruit young people who consult the ASSIR units; (3) young people who access the WSJ of their own accord will be invited to participate. These three methods will be used to invite participants to join the study; participation will be voluntary. Recruitment is more fully developed in the data collection Based on the size of the effect of the differences in the assessment of the WSJ contents, and depending on whether they consulted the website of their own accord or not, a total sample of 776 participants will be required, accepting an alpha risk of 0.05 and a beta risk of 0.2 in a two-sided test. Up to 388 subjects will be necessary in the first group and 388 in the second to find a statistically significant proportion difference, expected to be of 0.5 in group 1 and 0.4 in group 2.Focus groups and in-depth interviews will be used to collect information. A focus group consists of a group interview led by a moderator using a topic/interview script  and paper version of the questionnaire (Teleform). The online questionnaire can be accessed via direct link or QR code. The questionnaire consists of 24 closed questions and 2 open-ended questions. The questionaire is available in At the education centres: The recruiting researchers will communicate with these centres to disseminate the questionnaire among students. The centre directors will decide if their centre will use the online or paper questionnaire.®, e-consultation).At the ASSIR units: a wide network of professionals are also researchers that will recruit for the study. The study will be disseminated through posters in the waiting rooms and at the counter of each ASSIR unit. These posters will contain the study instructions and the QR code for the questionnaire so that young people can quickly download it to their mobile phone and answer it. There will also be a paper version of the questionnaire for people unable to access it digitally. Digital users of the ASSIR will be sent the link to the questionnaire with information to encourage them to answer on the same platform they used for the consultation .Variables related to the WSJ: prior knowledge of the WSJ, use of the WSJ, usability of the WSJ, usefulness of the WSJ, rating of affective-sexual education content.Variables related to social media: ability to access to digital resources, use of other webpages of affective-sexual education, following affective-sexual education influencers, use of pornography webpages.Variables related to study recruitment: Participants from educational centers and ASSIR units will access the survey by indication of the professionals. The third contacting group will access it by themselves, without anyone’s indication. The sample calculation has been performed based on this variable.The focus groups will be audio- and video-recorded. The individual face-to-face interviews will only be audio-recorded. The transfer of voice and image rights in the case of focus groups and of voice rights only in the case of individual interviews will be requested. In the context of COVID-19 , we might audio- and video-record virtual interviews using Microsoft Teams. Participants must give their informed written consent. Participants under the age of 16 must also present the consent of their parent or legal guardian. This is known as double consent. Participants aged 16 to 18 are considered to be mature minors and will not need the consent of their parent or legal guardian. In the quantitative study, the questionnaires are anonymous and voluntary. Participants under the age of 16 must provide an informed consent form signed by them and their parent or legal guardian.This protocol follows the tenets of the Declaration of Helsinki . The resDue to COVID-19, Microsoft Forms and Microsoft Teams software might be used. In this case, we will guarantee data protection, in accordance with the Regulation (EU) 2016/679 of the European Parliament and Council of April 27 on Data Protection (RGPD) and the Spanish Organic Law 3/2018 of December 5, on the Protection of Personal Data and guarantee of digital rights. The online questionnaire can only be answered by people aged 16 and over since the necessary consent cannot be obtained from children under 16. Consequently, the first question in the online questionnaire is if they agree to answer it, and the second if they are 16 or older. Responses from those under 16 will be collected using the paper questionnaire at the education centres and ASSIR units.A discourse analysis (Why do they say this?) of participants in the focus groups and individual interviews will be conducted ,39. We sWe will perform a descriptive analysis of all variables, using frequencies and percentages for categorical variables and mean and standard deviation for age and years living in Spain. Familiarity with the website will be summarized with a percentage and confidence interval. The bivariant analysis will be performed using Pearson’s chi-squared test or Fisher’s exact test to find associations between the assessment of WSJ and the sociodemographic variables. If the bivariant analysis shows any associations, a multivariant analysis using a regression model with assessment of WSJ as the outcome variable is planned. The anlaysis will be performed disaggregating by gender. Significance level is set at 5%. SPSS statistical package for Windows, version 25.0, will be used for all analyses.We will apply the quality and rigour criteria proposed by Guba and Lincoln , i.e., cThis study should reveal the perceptions, needs and deficiencies regarding sex education of young people and help understand how they live, think and express their sexuality.Authors on affective-sexual health agree that sexual health programs are not effective without the experience and perspective of young people ,9,42.Often, young people find it difficult to make commitments, and thus effective recruitment becomes more complex. Double consent, required of people under the age of 16, demands more resources and further hinders participation. Despite these limitations and in agreement with the literature, the proposed methodology is considered adequate.The evaluation of the WSJ as a public health resource requires both (1) affective-sexual and gender diversity and (2) a socioeconomic perspective. The results of this study should provide the basis on which to build a new, effective model of affective-sexual education for young people.


Due to the dynamic nature of demand, it is becoming increasingly important for the fish industry to investigate the changing choice behaviors of consumers in the face of increasing demand. This research investigated the role of attitudes and socio-demographic characteristics, which are the main factors in the fish choice behavior of consumers and in fish consumption behavior. In this context, an ordered probit model was constructed to analyze the effect of attitudes and socio-demographic characteristics as independent variables on fish consumption and purchase intention as the dependent variables. In addition, descriptive statistics were also used to reveal the current preferences related to fish. The data required for the model and descriptive statistics were obtained from 421 participants using a cross-sectional consumer survey covering the main cities of the seven regions of Turkey. The results show that while consumers prefer fish more than red meat and less than poultry, they mostly buy fresh fish from fish markets. Moreover, taste, physical appearance, convenience, wild fish, and seller trust attitudes have a significant and positive relationship with the dependent variable (the frequency of fish purchase and consumption) and price has a negative and significant relationship. Moreover, an increase in education level has a positive and significant relationship with the frequency of fish consumption. The research results provide important suggestions and information for decision-makers in the fish industry to implement effective policies and meet the consumer expectations of producers and distributors in the fish industry. In addition, the current study provides guidance for future research. Food choice is a dynamic, multidimensional, and complex phenomenon influenced by many interconnected factors; so, it has been the focus of food market research analysis over the last 50 years ,2,3,4,5.Food choice behavior has also been affected by economic and social drivers such as increases in per capita income, changes in agri-food sector practices, diversification of production methods, fluctuating food prices, trade liberalization, supply chain developments, and the increased role of the media and marketing over the past several decades ,10,11,12The importance of the influencing categories is likely to vary between consumers and products, making it crucial to understand which factors are important among specific segments for evaluating food choice and the drivers behind it .Revealing the factors underlying consumers’ food preferences is one of the main issues that agri-food marketing experts have focused on for many years. Globalization and developments in communication opportunities have affected the speed and dynamics of changes in food preferences. Therefore, the scope of research in this area is expanding, and the frequency of updates is increasing.In marketing, understanding attitudes can help marketers predict consumer behavior . The difSince fish consumption has been associated with the healthy life concept and a balanced diet during the last decades, authorities have encouraged fish consumption ,25,26,27From 1961 to 2017, global fish consumption increased by 3.1% at a rate nearly twice that of the annual world population growth (1.6%) and faster than all other animal protein foods , which increased at a rate of 2.1% per year. Per capita, fish consumption increased by about 1.5% per year from 9.0 kg  in 1961 to 20.5 kg in 2018. In 2017, fish consumption accounted for 17% of the global population’s animal protein consumption and 7% of the total protein consumption ,30. The Consumer attitudes significantly impact fish consumption and buying decisions, so there is significant interest in learning more about the dynamics of choice behavior for seafood ,32. SeafAttitudes act as a mediator in consumers’ food purchasing behavior and affect the intention and consumption of a food product ,43. Thusp = 0.05). The sociodemographic characteristics of the sample are given in The quantitative data required to present the consumers’ fish purchase and consumption patterns and explain the relationship between attitudes and fish choice behavior were gathered by a cross-sectional survey. This survey was conducted in the major cities  of the seven geographical regions of Turkey during August–October 2021 by a commercial marketing agency (Ayna Market Research Co.). These cities were chosen due to their geographical dispersion and population sizes, making them a good representation of the entire country. The sample size allocated to each city was determined according to their respective populations. The questionnaire was applied to 450 consumers who were responsible for their households’ food purchases. After the preliminary examination, 421 were chosen for evaluation and used in the analysis. The final sample size was extracted from an infinite population with a confidence level of 95.5% lnA principal component analysis (PCA) with a varimax rotation was applied to dimensionality reduction in order to reduce the complexity of the model. Thus, it was possible to determine the basic dimensions of consumer participation in fish selection behavior ,52,53,54p < 0.05. To perform the data analysis, ordered probit regression was conducted using StataCorp. 2013. Stata Statistical Software (STATA) v13  and PCA, and descriptive statistics  were reached using Social Sciences (SPSS) v21.0 software  [The level of significance was set at M Corp.) .According to In this study, the general meat consumption distribution of the individuals was determined, and it was concluded that the average fish consumption was less than the consumption of red meat and more than the consumption of poultry (3.5 kg per month) .In this research, consumers’ purchasing place preferences for where to buy fish were also determined . AccordiThe results obtained within the scope of this research show that more than half of the consumers consume fish once a week or 2–3 times a month. Consumers prefer fish markets for their fish purchases, and their consumption preferences are mostly for fresh fish.The description and mean values of the socio-economic characteristics of the sampled individuals are given in The answers given to the questions regarding the attitudes toward fish consumption of the individuals are presented in Within the scope of this research, all attitudes that may be effective in fish consumption were evaluated, and those related to consumers’ purchase or consumption were determined with the help of the established probit model. Accordingly, taste, convenience, seller confidence, wild fish, and physical appearance attitudes were found to be positively related to fish purchase and consumption while the price was negatively related. In addition, the effect of sociodemographic characteristics on fish consumption or purchase was also included in this research model. In this context, a positive relationship between education level and fish consumption behavior was revealed.According to the results of the descriptive statistics, monthly fish consumption was determined to be higher than red meat and lower than poultry consumption. Middle-income families in Turkey mostly consume poultry for their meat requirements. The relatively low price of poultry is the main reason for this preference, especially in times of high inflation such as now. In addition, fish are mostly preferred by consumers with higher education levels ,31,57,58The descriptive statistics results also show that consumers mostly prefer fish markets for their fish purchases. Among the factors that cause the preference for purchasing in fish markets is the abundance of fish varieties, high probability of finding fresh fish, and relatively lower prices. The fact that fresh fish is the first preference supports the finding that individuals prefer fish markets, which are one of the first links in the supply chain and provide access to fresh fish at lower prices. This is also supported by the correlation between the purchase frequency and the price factor in the regression results.The majority of consumers preferred fresh fish instead of processed options. Considering the consumption habits of Turkish consumers, dried, salted, and smoked fish are much less preferred than fresh, canned, and frozen fish. Fresh fish is cheaper than processed fish, and habits and cultural factors are effective in the prominence of fresh fish as a fish consumption preference. In their studies,  also staAccording to the ordered probit regression results revealing the relationships between attitudes toward fish and the probability of fish consumption see , the attThere is strong evidence that taste is a good predictor of consumers’ food choices as a sensory attribute. Consumers often mention taste as a major factor in their food preference decisions ,61,62,63In many studies on fish consumption preferences, the importance of the taste variable for consumers has also been revealed. In their studies, ,67,68,69In many studies, price was determined as the leading factor among the barriers to fish consumption, together with the trust of the seller, presence of fishbone, availability, and smell ,69,70,71As previously mentioned, trust is another barrier to consuming fish or seafood products. Lack of trust in choosing and preparing seafood is one of the biggest barriers to seafood consumption ,34. ConsIn fish choice behavior, convenience is accepted as another important factor and can determine whether an individual consumes fish or not . The conIn the seafood sector, fish are sourced from aquaculture farms or caught in the wild . In manyThe effects of sociodemographic variables on the frequency of consumption were also evaluated within the scope of this study. The results show that among the variables of gender, age, education, income, and household size, education is the only variable associated with the consumption frequency. The positive relationship between education and consumption frequency shows that the higher the education level of the consumers, the higher the probability of an increase in the fish consumption frequency. This is in line with previous studies of ,69,76,77It can be concluded from the results of this study that greater importance should be given to the modernization of fish markets and more efficient supply chains should be established to deliver fresh fish products to consumers in Turkey. Promotional activities should be carried out for canned and frozen products, which have important industrial infrastructure opportunities in Turkey. Thus, convenience, an important attitude regarding fish, can also be achieved. Fish should be made to be a more accessible and consumable product by applying policies to reduce consumer prices. In Turkey, fish is usually cooked and served without using diverse receipts and culinary arts. We can state that applications that enhance the flavor of fish  will positively affect fish consumption. Diversity and value should be added to fish presentation by researching fish dishes and serving methods in countries with high fish consumption habits. Since consumers give importance to the physical appearance of fish, and this attitude is related to consumption, the appearance characteristics of fresh fish should be highlighted in the market.One of the main determinants of the pricing of fresh fish in Turkey is whether the fish is wild or from aquaculture. Consumers generally prefer wild fish to aquaculture fish, but wild fish costs about 20% more. There is no standard-setting application that enables them to distinguish whether the fish is wild or farmed, and they generally have to rely on the seller’s statement. Seafood fraud has recently increased by 60%, and the seafood industry has been hit particularly hard. Salmon, tuna, and halibut are just a few varieties that are commonly mislabeled in restaurants and grocery stores. A new blockchain network aims to eliminate fraud by tracking seafood from the point of capture to the kitchen. With the knowledge of where and how the fish are produced using QR code applications, the consumer can better know what they are buying. With the increase in these applications, the complexity of this matter may be solved to a great extent. However, the absence of QR codes and blockchain applications creates doubt in consumers, and their trust in the seller becomes very important. For this reason, consumers generally buy fish from people or institutions they know. Features such as the freshness or wildness of fish should be positioned according to market standards and consumer perceptions.With the help of these results, efficient marketing strategies and promotional practices can be created, and inefficient ones can be avoided. Thus, the research findings have important managerial and policy implications that concern various stakeholders.Some limitations of this study should be acknowledged. First, this study only focused on individuals’ fish purchasing behavior for their home consumption. However, it is known that there are options for away-from-home fish consumption such as fish restaurants, fast food, and cafes. Research can also be applied to the away-from-home consumption behaviors of consumers regarding fish. Second, continuing pandemic conditions during the research process forced this research to be conducted with a limited number of samples to prevent an impact on public health. Last, to avoid confusing results, only fish were focused on, neglecting other aquatic products. Similar studies can also be carried out for other types of seafood.


The journey to psychiatric rehabilitation, once prerogative of few brave explorers in the field of social psychiatry, is now being open to new different and ambitious trajectories, where new paths such as physical activity and artificial intelligence add to more consolidated approaches such as Psychodynamic and Cognitive Behavior Therapy, through collaborative research, attention to stigma and personal perspective of young mental health professionals.Ziebart et al.) that investigated how exercise and physical activity could influence psychotic symptoms, in people with psychosis, within a hospital setting. Twenty-four trials were included in the systematic review and 9 of them were included in the meta-analysis . The results showed that aerobic exercise, but not yoga, was associated with reduction of psychosis symptom severity scores, when compared to usual care.This Research Topic presents some interesting insights about different routes, that may represent ambitious challenges for the future of rehabilitation. We start with a review by a Canadian group  within Clinical Decision Support System (CDSS) and how it may help in the complex decisions making process that is often needed in situations covered by Psychiatric Advance Directives (PADs). After analyzing the use of new information technology tools and techniques for the improvement of the patient's hospital experience, also focusing on ethical issues, the authors conclude that AI should remain a decision support system as a partner of each party of the PAD contract. Patient's informed choice on the possibility to benefit of AI is central.Then, shifting to artificial intelligence, Muschalla et al.) compared patients receiving Psychodynamic therapy (PDT) and Cognitive behavior therapy (CBT). The study involved 73 cognitive behavior therapists and 58 psychodynamic psychotherapists, who reported about 188 CBT patients and 134 PDT patients. No significant socio-demographics differences emerged between PDT and CBT patients. Number of sessions was lower in CBT patients, who also showed longer duration of illness, more parallel medical treatments and higher rates of sick leave. Correspondingly, CBT therapists reported more sociomedical interventions, like interdisciplinary treatment, work oriented interventions, and social support. The authors conclude that the differences between PDT and CBT may be explained by the fact that PDT requires analytical capabilities on the side of the patient, which may exclude patients with social problems, with complex mental disorders like dementia, or with a lack of psychological insight. The authors consider CBT as a more problem-oriented intervention, that allows to treat a wider range of patients.Talking about psychotherapy, a German team . Two hundred and fifty-eight European early career psychiatrists, from 34 countries responded to a questionnaire regarding their research experience. Even if most participants were highly interested in research and highly satisfied with mentoring and publishing papers, they reported major obstacles toward their research activities, such as lack of time and funding.The attention to mental health professionals and to their personal perspective in the field of rehabilitation is also demonstrated in other papers published in this Research Topic. Wang et al.) validated a translated version of a 20-item scale for assessing stigma of mental illness in nursing (SASMIN). The scale includes three dimensions: Violence/Dangerousness, Disability, and Irresponsibility/Lack of Competence. Five hundred and one nursing students participated in the study. The scale allowed to identify the different dimensions of stigma associated with mental illness among nursing staff and thus may be used in the clinical practice to improve quality of care and patient's satisfaction.In this context, the issue of stigma was investigated from the perspective of young nurses: a Chinese research team  focused on recovery education for people transitioning from homelessness, in the context of services provided by Recovery Education Centers (RECs). The authors compared recovery outcomes of adults with history of homelessness and mental health challenges enrolled in a REC, to those of a matched control group receiving usual interventions. Mean change in perceived empowerment at 1 year from baseline was not significantly different between groups. However, in a post-hoc analyses comparing subgroups with 1–13 h and 14+ h of REC participation the group receiving 14+ h of intervention showed greater change in perceived empowerment compared to controls. Mean change in mastery was also significantly different for the intervention subgroup with 14+ h of REC compared to controls. In conclusion when applied with enough intensity, recovery education may be a helpful add-on to health and social services for homeless people with mental health problems.Finally, the issue of recovery from homelessness was analyzed by a Canadian study .The author confirms being the sole contributor of this work and has approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.