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The increasing incidence of P. knowlesi malaria infection among humans is a public health threat. This zoonotic disease is challenging to eliminate owing to the presence of animal reservoirs. Understanding the factors such as the community’s belief, social context, drivers, and barriers can provide insights into malaria preventive behavior. It is crucial to improve the current preventive measures. This study aims to achieve consensus among malaria experts based on evidence from literature reviews and experts’ opinions on possible factors influencing malaria preventive behavior among communities exposed to P. knowlesi malaria infection. A modified Delphi study protocol was developed to gather experts’ consensus on the study framework to explore the factors influencing preventive behavior among communities exposed to P. knowlesi malaria infection. The framework is adapted from the ideation model, and it is integrated with other relevant theories and extensive literature reviews. We will use the modified Delphi protocol to reach a consensus. The experts will respond to each questionnaire item and a related open-ended questionnaire. Consensus is predetermined at more than 70% agreement on the items. We will use descriptive statistics and thematic analysis to analyze the data. All experts will remain anonymous to maintain the characteristics of a traditional Delphi study.

To investigate how consensus is operationalized in Delphi studies and to explore the role of consensus in determining the results of these studies. Systematic review of a random sample of 100 English language Delphi studies, from two large multidisciplinary databases [ISI Web of Science (Thompson Reuters, New York, NY) and Scopus (Elsevier, Amsterdam, NL)], published between 2000 and 2009. About 98 of the Delphi studies purported to assess consensus, although a definition for consensus was only provided in 72 of the studies (64 a priori). The most common definition for consensus was percent agreement (25 studies), with 75% being the median threshold to define consensus. Although the authors concluded in 86 of the studies that consensus was achieved, consensus was only specified a priori (with a threshold value) in 42 of these studies. Achievement of consensus was related to the decision to stop the Delphi study in only 23 studies, with 70 studies terminating after a specified number of rounds. Although consensus generally is felt to be of primary importance to the Delphi process, definitions of consensus vary widely and are poorly reported. Improved criteria for reporting of methods of Delphi studies are required.

Background: Substantial declines in genital warts (GW) have been observed in countries with quadrivalent HPV vaccination programmes, with Australia showing the highest reductions due to early commencement and high vaccination coverage. There is a real potential to achieve GW elimination; however, no GW elimination definition exists. Taking Australia as a case study, we aimed to reach expert consensus on a proposed GW elimination definition using a modified Delphi process. Method: We used modelling and epidemiological data to estimate the expected number of new GW cases, from pre-vaccination (baseline) in 2006 to the year 2060 in Australian heterosexuals, men who have sex with men (MSM), and newly arrived international travellers and migrants. We used these data and the literature, to develop a questionnaire containing ten elimination-related items, each with 9-point Likert scales (1—strongly disagree; 9—strongly agree). The survey was completed by 18 experts who participated in a full day face-to-face modified Delphi study, in which individuals and then small groups discussed and scored each item. The process was repeated online for items where consensus (≥70% agreement) was not initially achieved. Median and coefficient of variation (COV) were used to describe the central tendency and variability of responses, respectively. Findings: There was a 95% participation rate in the face-to-face session, and 84% response rate in the final online round. The median item score ranged between 7.0 and 9.0 and the COV was ≤0.30 on all items. Consensus was reached that at ≥80% HPV vaccination coverage, GW will be eliminated as a public health problem in Australia by 2060. During this time period there will be a 95% reduction in population-level incidence compared with baseline, equivalent to <1 GW case per 10,000 population. The reductions will occur most rapidly in Australian heterosexuals, with 73%, 90% and 97% relative reductions by years 2021, 2030 and 2060, respectively. The proportion of new GW cases attributable to importation will increase from 3.6% in 2006 to ~49% in 2060. Interpretation: Our results indicate that the vaccination programme will minimise new GW cases in the Australian population, but importation of cases will continue. This is the first study to define GW elimination at a national level. The framework developed could be used to define GW elimination in other countries, with thresholds particularly valuable for vaccination programme impact evaluation. Funding: LK supported through an Australian Government Research Training Programme Scholarship; unconditional funding from Seqirus to support the Delphi Workshop.

Background Scores on an outcome measurement instrument depend on the type and settings of the instrument used, how instructions are given to patients, how professionals administer and score the instrument, etc. The impact of all these sources of variation on scores can be assessed in studies on reliability and measurement error, if properly designed and analyzed. The aim of this study was to develop standards to assess the quality of studies on reliability and measurement error of clinician-reported outcome measurement instruments, performance-based outcome measurement instrument, and laboratory values. Methods We conducted a 3-round Delphi study involving 52 panelists. Results Consensus was reached on how a comprehensive research question can be deduced from the design of a reliability study to determine how the results of a study inform us about the quality of the outcome measurement instrument at issue. Consensus was reached on components of outcome measurement instruments, i.e. the potential sources of variation. Next, we reached consensus on standards on design requirements ( n  = 5), standards on preferred statistical methods for reliability ( n  = 3) and measurement error ( n  = 2), and their ratings on a four-point scale. There was one term for a component and one rating of one standard on which no consensus was reached, and therefore required a decision by the steering committee. Conclusion We developed a tool that enables researchers with and without thorough knowledge on measurement properties to assess the quality of a study on reliability and measurement error of outcome measurement instruments.

Purpose Despite the variety of supply chain management (SCM) research, little attention has been given to the use of Big Data Analytics for increased information exploitation in a supply chain. The purpose of this paper is to contribute to theory development in SCM by investigating the potential impacts of Big Data Analytics on information usage in a corporate and supply chain context. As it is imperative for companies in the supply chain to have access to up-to-date, accurate, and meaningful information, the exploratory research will provide insights into the opportunities and challenges emerging from the adoption of Big Data Analytics in SCM. Design/methodology/approach Although Big Data Analytics is gaining increasing attention in management, empirical research on the topic is still scarce. Due to the limited availability of comparable material at the intersection of Big Data Analytics and SCM, the authors apply the Delphi research technique. Findings Portraying the emerging transition trend from a digital business environment, the presented Delphi study findings contribute to extant knowledge by identifying 43 opportunities and challenges linked to the emergence of Big Data Analytics from a corporate and supply chain perspective. Research limitations/implications These constructs equip the research community with a first collection of aspects, which could provide the basis to tailor further research at the nexus of Big Data Analytics and SCM. Originality/value The research adds to the existing knowledge base as no empirical research has been presented so far specifically assessing opportunities and challenges on corporate and supply chain level with a special focus on the implications imposed through Big Data Analytics.

A Delphi study using twenty-three project practitioners over six rounds is aimed to identify significant problems in project management, arising from the nature of projects other than those readily identifiable in a literature review. The study goes on to identify project managers' behaviours which are recognised as having successful impacts on the delivery of projects. A Relative Importance Index for the problems and behaviours resulting from the issues identified in the study is calculated. This study continues by reporting the views of practitioners involved in the management of large projects on the everyday problems they experience in managing projects, problems that are not adequately addressed in current project management texts concerning the nature of projects. Five additional problems, not generally discussed in the literature, were identified together with seven interpersonal skills and behaviours that are major contributors to increasing the likelihood of a successful project delivery.

A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications. The aim of this adapted Delphi study was to collect researchers' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care. We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items. The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data. Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.

The logistics industry is facing a transformation. Automated driving has been gaining importance in the commercial vehicle industry and trucks with SAE L4 are expected by 2030 for the hub-to-hub scenario. Driven by the research question of what the direct logistics environment of automated trucks will look like in 2030 a two-round Delphi-based scenario study was conducted for domestic goods transport in Germany. 19 projections were developed and evaluated by 27 experts from different industries. With completelinkage clustering, four logistics scenarios for 2030 were created. The results show that environmental and social sustainability as well as digitalization are expected to be the most important drivers. These include the shift to electric drive systems, improved working conditions, and increasing transparency and connectivity of the supply chain. The experts forecast an increase in the importance of software services and a continuing shortage of skilled workers. Rather controversial are the topics of charging infrastructure for electrified transport and the degree of automation of loading systems. Overall, the results provide a reliable basis for strategic decision-making in order to ensure the introduction of automated trucks into the logistics of the future and their surrounding environment.

Process mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of process mining in organizations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using process mining in organizations. Such an understanding has the potential to guide research by highlighting barriers for process mining adoption and, thus, can contribute to successful process mining initiatives in practice. In this respect, the paper provides a holistic view of opportunities and challenges for process mining in organizations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of individual items, as well as differences in the perceived relevance between academics and practitioners. Therefore, the study contributes to the future development of process mining, both as a research field and regarding its application in organizations.

Background In cooperation with the Core Outcome Measures in Effectiveness Trials (COMET) initiative, the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) initiative aimed to develop a guideline on how to select outcome measurement instruments for outcomes (i.e., constructs or domains) included in a “Core Outcome Set” (COS). A COS is an agreed minimum set of outcomes that should be measured and reported in all clinical trials of a specific disease or trial population. Methods Informed by a literature review to identify potentially relevant tasks on outcome measurement instrument selection, a Delphi study was performed among a panel of international experts, representing diverse stakeholders. In three consecutive rounds, panelists were asked to rate the importance of different tasks in the selection of outcome measurement instruments, to justify their choices, and to add other relevant tasks. Consensus was defined as being achieved when 70 % or more of the panelists agreed and when fewer than 15 % of the panelists disagreed. Results Of the 481 invited experts, 120 agreed to participate of whom 95 (79 %) completed the first Delphi questionnaire. We reached consensus on four main steps in the selection of outcome measurement instruments for COS: Step 1, conceptual considerations; Step 2, finding existing outcome measurement instruments, by means of a systematic review and/or a literature search; Step 3, quality assessment of outcome measurement instruments, by means of the evaluation of the measurement properties and feasibility aspects of outcome measurement instruments; and Step 4, generic recommendations on the selection of outcome measurement instruments for outcomes included in a COS (consensus ranged from 70 to 99 %). Conclusions This study resulted in a consensus-based guideline on the methods for selecting outcome measurement instruments for outcomes included in a COS. This guideline can be used by COS developers in defining how to measure core outcomes.