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Are reviews with photos more helpful? If so, do consumers find reviews more helpful when photos and text convey similar or different information? This article examines the effect of content similarity between photos and text on review helpfulness and its underlying mechanism. Using a data set of 7.4 million reviews associated with 3.5 million photos from Yelp, and applying machine learning algorithms, the authors quantify the similarity of the content between text and photos. They find that, overall, photos increase the helpfulness of a review. More importantly, though, greater similarity between photos and text heightens review helpfulness more. The authors then validate algorithm-based similarity assessments with similarity perceptions of human judges. Using real-world reviews from Yelp and carefully designed stimuli, they replicate the core findings in five laboratory experiments. Further, testing the underlying mechanism, they find that greater similarity facilitates the ease with which consumers can process the review, which, in turn, increases that review's helpfulness to consumers. Finally, they show that factors that impede the ease of processing (e.g., language difficulty or poor image quality) can reduce the effect of similarity on helpfulness. These findings provide novel insights into the value of user-generated content that includes text and photos and its underlying mechanism. [ABSTRACT FROM AUTHOR]

Meta-analysis is a powerful tool in sport and exercise psychology. However, it has a number of pitfalls, and some lead to ill-advised comparisons and overestimation of effects. The impetus for this research note is provided by a recent systematic review of meta-analyses that examined the correlates of sport performance and has fallen foul of some of the pitfalls. Although the systematic review potentially has great value for researchers and practitioners alike, it treats effects from correlational and intervention studies as yielding equivalent information, double-counts multiple studies, and uses an effect size for correlational studies (Cohen's d) that provides an extreme contrast of unclear practical relevance. These issues impact interpretability, bias, and usefulness of the findings. This methodological note explains each pitfall and illustrates use of an appropriate equivalent effect size for correlational studies (Mathur and VanderWeele's d) to help researchers avoid similar issues in future work. [ABSTRACT FROM AUTHOR]

Melanoma, a highly prevalent and lethal form of skin cancer, has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the early identification of melanoma. Despite their high performance, relying solely on an image classifier undermines the credibility of the application and makes it difficult to understand the rationale behind the model's predictions highlighting the need for Explainable AI (XAI). This study provides a survey on skin cancer identification using DL techniques utilized in studies from 2017 to 2024. Compared to existing survey studies, the authors address the latest related studies covering several public skin cancer image datasets and focusing on segmentation, classification based on convolutional neural networks and vision transformers, and explainability. The analysis and the comparisons of the existing studies will be beneficial for the researchers and developers in this area, to identify the suitable techniques to be used for automated skin cancer image classification. Thereby, the survey findings can be used to implement support applications advancing the skin cancer diagnosis process. [ABSTRACT FROM AUTHOR]

The high-cost pressure caused by the level of competition poses major challenges for breweries. While microbreweries can develop local strengths and brewery groups develop synergies, this does not represent a decisive improvement. The application of machine learning, on the other hand, could give breweries a significant advantage in their brewing process. Several approaches to the application of machine learning in the brewing process have already been proposed in the literature. To guide possible areas of applications and the respective available solution approaches to improve the brewing process based on machine learning, a systematic review of the application of machine learning in the brewing process is presented in this paper. In this systematic review, all potentially relevant publications were included at first. Subsequently, irrelative publications were filtered out by using a clustering approach. Afterward, the remaining 21 publications were analyzed and synthesized. Based on a developed framework considering the brewing process steps, areas of improvement, machine learning tasks, and machine learning algorithms, these publications were classified. Upon the classification, a descriptive analysis was performed to identify common approaches in the existing literature. One result was that research on artificial intelligence in brewing lags significantly behind the general trend of artificial intelligence research. Additionally, there is very limited research into the association between the recipe and the desired chemical properties of the beer. Furthermore, it was noticeable that machine learning tasks utilizing artificial neural networks or support vector machines were preferred over others. [ABSTRACT FROM AUTHOR]

Mobility-as-a-service (MaaS) integrates different transport modalities and can support more personalization of travelers’ journey planning based on their individual preferences, behaviors and wishes. To fully achieve the potential of MaaS, a range of artificial intelligence (AI) (including machine learning and data mining) algorithms are needed to learn personal requirements and needs to optimize the journey planning of each traveler and all travelers as a whole, to help transport service operators and relevant governmental bodies to operate and plan their services, and to detect and prevent cyberattacks from various threat actors, including dishonest and malicious travelers and transport operators. The increasing use of different AI and data processing algorithms in both centralized and distributed settings opens the MaaS ecosystem up to diverse cyber and privacy attacks at both the AI algorithm level and the connectivity surfaces. In this article, we present the first comprehensive review on the coupling between AI-driven MaaS design and the diverse cybersecurity challenges related to cyberattacks and countermeasures. In particular, we focus on how current and emerging AI-facilitated privacy risks (profiling, inference, and third-party threats) and adversarial AI attacks (evasion, extraction, and gamification) may impact the MaaS ecosystem. These risks often combine novel attacks (e.g., inverse learning) with traditional attack vectors (e.g., man-in-the-middle attacks), exacerbating the risks for the wider participation actors and the emergence of new business models. [ABSTRACT FROM AUTHOR]

AbstractThe study deals with understanding of the factors associated with online ratings and reviews that help the healthcare consumers in better decision making. Data was collected from 303 participants using mixed methods. The results indicate the factors that help in decision making which includes use of ratings as a reference, impact of positive and negative ratings, whether these ratings depict the actual worth of healthcare service provider and the relevance of these websites in decision making. This study concludes that there is significant relevance of online rating and review websites on healthcare consumers in selecting a healthcare provider. [ABSTRACT FROM AUTHOR]

Cognitive impairment and older age are major risk factors for postoperative delirium. Professional societies have advocated preoperative screening to identify at-risk individuals for implementation of interventions, which have moderate effectiveness in preventing delirium. However, it remains unclear from the guidelines whether screening should be completed routinely for all older individuals or targeted, and also which specific screening tool is preferred. In addition, the responsibility for screening remains undesignated in the guidelines provided. We reviewed the benefits and risks of routine screening. We also performed a cost-benefit analysis of routine screening (versus no screening). Furthermore, we summarised the sensitivities and specificities of commonly used screening tools and reviewed evolving screening tools that may have an increasing role in future practice. We concluded that routine screening is useful and appears to be cost-effective for reducing postoperative delirium, with a cost-benefit ratio of 2.89 (adjusted ratio of 2.34), and has additional advantages over other approaches such as targeted screening or routine intervention. [ABSTRACT FROM AUTHOR]

It is generally recognized that accurate and timely prediction of future traffic flow information is one of the important conditions for improving the utilization rate of road networks and relieving traffic congestion. Since long short-term memory (LSTM) networks have obvious advantages in dealing with time series data, LSTM-based prediction models, as well as the improved and hybrid models based on LSTM, have been widely used in the field of traffic flow prediction and have achieved a series of important results. However, although some existing reviews have provided a comprehensive summary of and prospects for traffic flow prediction methods, there is no recent review specifically summarizing the traffic flow prediction methods based on LSTM. Therefore, this article presents a comprehensive review of LSTM-based methods for traffic flow prediction. First, we briefly introduce the development history of traffic flow prediction, the advantages of using LSTM for traffic flow prediction, and the basic principles of LSTM. Then, the representative model structures, training methods, public datasets, attractive results, the advantages and disadvantages of different LSTM-based methods are presented and analyzed in detail. Furthermore, to illustrate the performance of various refinements and hybrid models based on LSTM for traffic flow prediction, we meticulously select several representative approaches to conduct comparative experiments and quantitative analysis. Finally, we discuss the current limitations and challenges of LSTM and traffic flow prediction and present potential areas of future research interest to the readers. [ABSTRACT FROM AUTHOR]

Market producers often seek to position themselves in different categories over time. Successful repositioning is difficult, however, as audiences often devalue offerings that depart from a producer's past creations. Prior research suggests that this penalty arises as evaluators withhold opportunities for producers to reposition because of presumptions of a lack of competence in different categories. In this paper, we develop understanding of a novel evaluator-driven challenge to producers' repositioning efforts: evaluators are prone to "categorical stickiness," by which the categories they have come to associate with a producer through its prior offerings shape their perceptions of the producer's subsequent offerings. The result is a systematic mismatch between what producers claim and what evaluators perceive when a producer repositions. We further propose that audience members who have the greatest prior experience with a producer are the least likely to recognize its repositioning efforts. We examine evidence for our theory using data from Goodreads.com on authors within the book publishing industry, 2007–2017. We first build a novel deep-learning framework to predict categorization of a given book based solely on an author's description of its content. We then use data on how Goodreads users categorize and evaluate books as well as their past reading behavior to test for evidence of our proposed mechanism. Overall, our results extend understanding of the evaluative processes that generate categorical constraints and how these may differ among various types of audience members. This paper was accepted by Isabel Fernandez-Mateo, organizations. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2021.02070. [ABSTRACT FROM AUTHOR]

The purpose is to examine the memoir component of the “Ukrainian Historian”, the main printed organ of diaspora scholars. In particular, to identify the main groups of materials, the author’s composition, thematic focus, and the tasks set by the editorial board in publishing memoir narratives and research on them. The methodology is based on the use of historical and scientific analysis, systematization, chronological, source and textual methods, which made it possible to determine the species composition of memoir publications, their information potential, and their significance for historical science and society. The scientific novelty lies in the systematization of the memoir component of the journal, the identification of the main groups, including memoirs of Ukrainian figures, memories of them and events of Ukrainian history or institutions, source and archaeological publications, as well as reviews for the entire period of its publication. The main authors / “heroes”, the topics of the memoirs, and their significance for historical science and society are identified. Particular attention is paid to reviews of memoirs as a type of scientific testing, which plays an important role in the expert evaluation and promotion of memoirs among the scientific community and the public. Conclusions. From the very first issues, the editorial board of the Ukrainian Historian launched special sections devoted to memoirs. This was a recognition of their importance for Ukrainian scholarship in the context of historical and biographical research as a thorough addition to archival materials, which significantly expanded the source base of Ukrainian historiography in the diaspora and Ukraine. The “Ukrainian Historian” has become an important alternative platform for publishing Ukrainian memoirs and research on them, and thus for testing their authenticity and source value, as opposed to silencing them and preventing their dissemination as stigmatized “ideologically harmful” and “bourgeois nationalist” literature in the Soviet public and scientific information space. [ABSTRACT FROM AUTHOR]

Reviews of Becoming a Reading Teacher: Connecting Research and Practice, Jane Spiro & Amos Paran (eds) (2023), This is My Story: Biographical and Autobiographical Narratives in English Language Teaching, Michael C. Prusse & Nikola Mayer (eds) (2024) and Re-thinking Picturebooks for Intermediate and Advanced Learners: Perspectives for Secondary English Language Education, Grit Alter & Thorsten Merse (eds) (2023)

Abstract The high-cost pressure caused by the level of competition poses major challenges for breweries. While microbreweries can develop local strengths and brewery groups develop synergies, this does not represent a decisive improvement. The application of machine learning, on the other hand, could give breweries a significant advantage in their brewing process. Several approaches to the application of machine learning in the brewing process have already been proposed in the literature. To guide possible areas of applications and the respective available solution approaches to improve the brewing process based on machine learning, a systematic review of the application of machine learning in the brewing process is presented in this paper. In this systematic review, all potentially relevant publications were included at first. Subsequently, irrelative publications were filtered out by using a clustering approach. Afterward, the remaining 21 publications were analyzed and synthesized. Based on a developed framework considering the brewing process steps, areas of improvement, machine learning tasks, and machine learning algorithms, these publications were classified. Upon the classification, a descriptive analysis was performed to identify common approaches in the existing literature. One result was that research on artificial intelligence in brewing lags significantly behind the general trend of artificial intelligence research. Additionally, there is very limited research into the association between the recipe and the desired chemical properties of the beer. Furthermore, it was noticeable that machine learning tasks utilizing artificial neural networks or support vector machines were preferred over others.

Abstract Melanoma, a highly prevalent and lethal form of skin cancer, has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the early identification of melanoma. Despite their high performance, relying solely on an image classifier undermines the credibility of the application and makes it difficult to understand the rationale behind the model's predictions highlighting the need for Explainable AI (XAI). This study provides a survey on skin cancer identification using DL techniques utilized in studies from 2017 to 2024. Compared to existing survey studies, the authors address the latest related studies covering several public skin cancer image datasets and focusing on segmentation, classification based on convolutional neural networks and vision transformers, and explainability. The analysis and the comparisons of the existing studies will be beneficial for the researchers and developers in this area, to identify the suitable techniques to be used for automated skin cancer image classification. Thereby, the survey findings can be used to implement support applications advancing the skin cancer diagnosis process.

The Peer-to-Peer sector of the sharing economy relies on reputation systems through which consumers and providers review each other. Whereas prior research has examined the effects of reviews by consumers on providers and firms, this research examines, for the first time, a turn of the tables in which consumers are evaluated. Across a pilot and seven studies (five preregistered), using multiple actual behaviors and sharing contexts, results reveal that a negative review of the consumer from the peer provider leads to negative word of mouth (NWOM) about the platform. Drawing from psychological contract theory, the research demonstrates that this effect is mediated by consumers' perceived betrayal by the platform. Two sets of moderators are identified. The first set establishes that a breach of consumers' psychological contract with the platform underlies the effect. In the second set, platform policies that may render a breach more or less consequential can intensify or mitigate consumer reactions. Specifically, making the review private (vs. public) and providing opportunities for justice restoration (response, revenge, and dispute) attenuate the effect of review valence on betrayal and NWOM. Implications for sharing economy platform managers and consumers are discussed. [ABSTRACT FROM AUTHOR]

Non-phthalate plasticizers are being increasingly used in commercial and consumer products, to replace phthalates. Among major non-phthalate plasticizer groups, we chose five alternative plasticizers of DEHA, DINCH, DEHTP, ATBC, and TOTM, and conducted a systematic literature review for current knowledge on their use and occurrences in major sources, and their exposure and endocrine outcomes in humans. Relevant articles published between January 2000 and December 2022 were identified from PubMed and Scopus search and analyzed. For occurrence, biomonitoring, and endocrine-related outcomes, 79, 73, and 14 studies were finally identified, respectively. The alternative plasticizers were widely used in food packaging, children's products, hygiene products, medical devices, and construction materials, and frequently detected in indoor dust. Food packaging materials and children's products were major sources of direct exposure to humans. Human exposure to alternative plasticizers is reported mostly via biomonitoring of urine. Urinary levels of DEHTP and DINCH metabolites were higher among infants, children, and pregnant women than in general adults. In humans, DEHTP, DINCH, or ATBC exposure were associated with sex hormone disruption and reproductive outcomes, but these associations varied by sex and age. This review showed that the use of and exposure to these alternative plasticizers have increased over time. Their potential health implications, especially among susceptible humans, warrant greater attention and further investigation. [ABSTRACT FROM AUTHOR]

The popular belief is that expensive wines are better than cheaper ones. However, there is a lack of previous studies exploring if it is effectively true. In this research, we explored this issue using three datasets. The results confirm the beliefs that expensive wines are better rated. Also, there is a difference between specialists compared with average consumers. Specifically, average consumers are more sensitive to price. Further, this effect is mediated by positive emotions expressed in the reviews and moderated by the power perceived by consumers. We contribute to previous literature by demonstrating that wine prices increase people's perceptions of wine quality and that the perceptions of wine differ from specialists to aspirants and regular customers. We tested these contributions using secondary data from wine websites and social networks. This methodology can be employed by managers to monitor their consumers and understand their behaviors. [ABSTRACT FROM AUTHOR]

How do cultural critics in the digital age convince audiences that their writings are valuable? What discursive strategies do they employ to construct their authority? And which differences can we see between professional critics working in institutionalized media and amateur critics contributing to online platforms? This article presents an in-depth analysis of book reviews by different critics to answer these questions. The results indicate that long-standing critical strategies are still largely intact: both professional and amateur critics construct authority by analyzing the book, contextualizing the book and discussing its reception, suggesting that amateurs have adopted to a large degree the skill sets of professionals. At the same time, amateur critics distinguish themselves by a pronounced presence of their personal experience in their reviews. This could point to a new way of constructing authority. [ABSTRACT FROM AUTHOR]

The article examines the limitations of the H-index as a research metric for evaluating the impact of experimental scientists. It presents a case study of six scientists in the biomolecular sciences, comparing their H-index and average citations per year. The study finds that a scientist without major awards, referred to as Scientist X, has a higher H-index and average citations per year than prize-winning scientists, primarily due to publishing review and perspective articles instead of conducting original experimental research. The article proposes a new metric, the EH-index, which would weigh citation counts based on the type of research contribution, specifically focusing on citations from experimental work. The author argues that the H-index fails to distinguish between original research studies and literature-based ones, and suggests further investigation into the impact of overpublishing secondary source articles on research metrics. [Extracted from the article]

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On most e-commerce websites, there are two crucial factors that customers rely on to assess product quality and dependability: customer reviews provided online and related ratings. Reviews offer feedback to customers about the product's merits, reasons for negative reviews, and feelings of satisfaction or dissatisfaction with the provided service. As for ratings, they express customer opinions about the product's quality as numerical values from one to five (one or two for the worst opinion, three for the neutral opinion, and four or five for the best opinion). Usually, the customer reviews may be inconsistent with their relevant ratings; the customer may write the worst review despite providing a four- or five-star rating or write the best review with only a one- or two-star rating. Due to this inconsistency, customers may need help to identify relevant information. Therefore, it is required to develop a model that can classify reviews as either positive or negative, depending on the polarity of thoughts, to demonstrate if there is an inconsistency between customer reviews and their actual ratings by comparing them with the ratings resulting from the model. This paper proposes an efficient deep learning (DL) model for classifying customer reviews and assessing whether there is inconsistency. The recommended model's performance and stability are examined on a large dataset of product reviews from Amazon e-commerce. The experimental findings showed that the proposed model dominates and significantly outperforms its peers regarding prediction accuracy and other performance measures. [ABSTRACT FROM AUTHOR]

AbstractStreamers have become a crucial component of consumers’ live shopping experience. Reviewers will transfer their emotions about the streamer to the product-after review. We innovatively verified the reverse effect of reviewers’ sentiment tendency toward streamer on the product sustained sale in the streamer’s live streaming. Results from a field data set and three lab experiments show that reviewers’ positive sentiment tendency toward the streamer has a negative effect on the readers’ purchase intention when the streamer resale this product in his live streaming. While reviewers’ negative sentiment tendency toward the streamer can enhance readers’ purchase intention to the product resold in the streamer’s live streaming. These effects are separately mediated by readers’ trust in the review and their empathy to the streamer. And the two mediators are moderated by the streamer’s expertise. Our findings expand the literatures about reviews’ emotions and suggest implications for streamers, retailers, and review platforms faced with the task of managing live-shopping reviews. [ABSTRACT FROM AUTHOR]

The proliferation of reviews evaluating different services on social networks and online platforms and their importance in consumer decision‐making has led some unscrupulous individuals to take advantage of the anonymity offered by the Internet to manipulate these reviews and influence customers' decisions. The main objectives of this study are: (1) to test whether spammers usually perform their misdemeanors from the same IP address; (2) to explore whether there are differences between stated sexes in this regard; (3) to detect the main motivations for posting fraudulent reviews; and (4) to determine the motivations for doing so from the same IP address. These objectives were achieved by means of a quasi‐experiment with a sample of 7,192,487 users, and a qualitative investigation in which 37 users who had falsified information were interviewed. The results show that spammers who tend to fake their identity do so from the same IP address and that they tend to be male. Four types of motivation are presented: revenge, entertainment, opportunity for profit, and self‐esteem; as well as a further three to explain the use of the same IP: convenience, limited resources, and complacency. [ABSTRACT FROM AUTHOR]

Based on content analysis (in the context of the historical, socio-cultural and political situation, etc.) of texts published during the "stagnant" period of the Soviet Screen magazine (1969--1985), the authors came to the conclusion that the principle of choosing cinematic personalities to paint portraits of Western actors and directors in 1969-1985 remained stable in the Soviet Screen. This magazine readily wrote about "progressive filmmakers" who went unnoticed in negative statements against the USSR and participation in anti-Soviet films. Especially if these filmmakers came to the Moscow International Festival and spoke positively about the Soviet Union. One of the favorite subjects of the Soviet Screen within the framework of foreign themes is Western actors as "victims of Hollywood", evidence of the "tragedy of the artist in the bourgeois world." But if any of the famous Western actors was involved in the creation of anti-Soviet films and/or films "glorifying the American military," the magazine could strike him with "heavy artillery" on its pages. However, there were many less politicized articles about Western actors on the pages of the magazine. As for interviews with Western filmmakers, as in previous years, the Soviet Screen magazine selected interlocutors from among the most "progressive artists." For example, among American filmmakers, the first place was given to Stanley Kramer. The progressive political position was also emphasized in interviews with Sydney Pollack, Norman Jewison, Ellen Burstyn, Jessica Lange and other Western directors and actors. In general, during the period 1969-1985, the ideological positions of the Soviet Screen magazine remained unchanged. [ABSTRACT FROM AUTHOR]

Based on the content analysis (in the context of the historical, socio-cultural and political situation, etc.) of the texts published during the "perestroika" period of the Soviet Screen magazine (1986--1991), the authors came to the conclusion that materials on the subject of Western cinema on this stage can be divided into the following genres: -- ideologized articles emphasizing criticism of bourgeois cinema and its harmful influence on the audience (1986-1987); -- articles on the history of Western cinema; -- biographies and creative portraits of Western actors and directors (as a rule, with positive ratings); -- interviews with Western filmmakers (as a rule, with those who came to Moscow film festivals); -- reviews of Western films (here we can note a violation of the old tradition: if in 1986-1987 the magazine still often negatively evaluated some "politically harmful bourgeois" films, then later Western film production was evaluated without regard to ideological stereotypes, moreover, even received a positive interpretation films that were previously rejected for ideological reasons); -- articles about international film festivals and weeks of foreign cinema in the USSR, reviews of the current repertoire of Western national cinematographies (no longer divided into "progressive" and "bourgeois" cinematography); -- short informational materials about events in Western cinema (from neutral reports to "yellow" gossip). [ABSTRACT FROM AUTHOR]

This study examines significant topics and customer sentiments conveyed in reviews of P2P lending applications (apps) in India by employing topic modeling and sentiment analysis. The apps considered are LenDenClub, Faircent, i2ifunding, India Money Mart, and Lendbox. Using Latent Dirichlet Allocation, we identified and labeled 11 topics: application, document, default, login, reject, service, CIBIL, OTP, returns, interface, and withdrawal. The sentiment analysis tool VADER revealed that most users have positive attitudes toward these apps. We also compared the five apps overall and on specific topics. Overall, LenDenClub had the highest proportion of positive reviews. We also compared the prediction abilities of six machine-learning models. Logistic Regression demonstrates high accuracy with all three feature extraction techniques: bag of words, term frequency-inverse document frequency, and hashing. The study assists borrowers and lenders in choosing the most appropriate application and supports P2P lending platforms in recognizing their strengths and weaknesses.

This article draws attention to the transformation of the Swiss physician Johann Georg Zimmermann’s (1728–1795) work on national pride. First published as Von dem Nationalstolze in 1758, this work attracted trans-European interest and consequently appeared in substantially revised editions in 1760 and 1768. One notable addition in the new editions was a chapter on national pride felt by the subjects of monarchies, which could be taken as indicating a monarchist turn in Zimmermann’s thinking. However, as the article contends, Zimmermann’s work can be read not only as a treatise making claims about the national pride of various countries but also as a text which, in so doing, attempted to encourage liberty and patriotic virtue in different societies. As such, it can be interpreted as representing a variation of republicanism that focussed solely on reforming the thinking of individuals. This argument is based on an analysis of the evolution of Zimmermann’s work, his correspondence, contemporary reviews, and a set of unpublished draft prefaces. As this evidence indicates, Zimmermann aspired to promote a republican attitude wherever his readers were situated. However, appreciating this ‘internalised republicanism’ requires that his writing is examined as a process involving the reactions of his expanding audience. [ABSTRACT FROM AUTHOR]

The objective of this study was to examine the conceptualisation and operationalisation of Integrated Disease Surveillance (IDS) systems globally and the evidence for their effectiveness. Furthermore, to determine whether the recommendations made by Morgan et al. are supported by the evidence and what the evidence is to inform country development of IDS. The study incorporated a scoping review. This review summarised evidence meeting the following inclusion criteria: Participants : any health sector; Concept : IDS; and Context : global. We searched Medline, Embase, and Epistemonikos for English publications between 1998 and 2022. Standard review methods were applied. A bespoke conceptual framework guided the narrative analysis. This scoping review is part of a research programme with three key elements, with the other studies being a survey of the International Association of National Public Health Institutes members on the current status of their disease surveillance systems and a deeper analysis and case studies of the surveillance systems in seven countries, to highlight the opportunities and challenges of integration. Eight reviews and five primary studies, which were assessed as being of low quality, were included, mostly examining IDS in Africa, the human sector, and communicable diseases. None reported on the effects on disease control or on the evolution of IDS during the COVID-19 pandemic. Descriptions of IDS and of integration varied. Prerequisites of effective IDS systems mostly related to the adequacy of core functions and resourcing requirements. Laws or regulations supporting system integration and data sharing were not addressed. The provision of core functions and resourcing requirements were described as inadequate, financing as non-sustainable, and governance as poor. Enablers included active data sharing, close cooperation between agencies, clear reporting channels, integration of vertical programs, increased staff training, and adopting mobile reporting. Whilst the conceptual framework for IDS and Morgan et al.'s proposed principles were to some extent reflected in the highlighted priorities for IDS in the literature, the evidence base remains weak. Available evidence is fragmented, incomplete, and of poor quality. The review found a lack of robust evaluation studies on the impact of IDS on disease control. Whilst a lack of evidence does not imply a lack of benefit or effect, it should signal the need to evaluate the process and impact of integration in the future development of surveillance systems. A common IDS definition and articulation of the parts that constitute an IDS system are needed. Further robust impact evaluations, as well as country reviews and evaluations of their IDS systems, are required to improve the evidence base. [ABSTRACT FROM AUTHOR]