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While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies. The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists. By understanding these variations, we aim to provide guidelines for the development of AI systems that can support optometrists with varying levels of expertise. These guidelines will assist in developing AI systems for glaucoma diagnosis, ultimately enhancing the diagnostic accuracy of optometrists and minimizing inconsistencies in their decisions. We conducted in-depth interviews with 14 optometrists using within-subject design, including both novices and experts, focusing on their approaches to glaucoma diagnosis. The responses were coded and analyzed using a mixed method approach incorporating both qualitative and quantitative analysis. Statistical tests such as Mann-Whitney U and chi-square tests were used to find significance in intergroup variations. These findings were further supported by themes extracted through qualitative analysis, which helped to identify decision-making patterns and understand variations in their approaches. Both groups showed lower concordance rates with clinical diagnosis, with experts showing almost double (7/35, 20%) concordance rates with limited data in comparison to novices (7/69, 10%), highlighting the impact of experience and data availability on clinical judgment; this rate increased to nearly 40% for both groups (experts: 5/12, 42% and novices: 8/21, 42%) when they had access to complete historical data of the patient. We also found statistically significant intergroup differences between the first visits and subsequent visits with a P value of less than .05 on the Mann-Whitney U test in many assessments. Furthermore, approaches to the exam assessment and decision differed significantly: experts emphasized comprehensive risk assessments and progression analysis, demonstrating cognitive efficiency and intuitive decision-making, while novices relied more on structured, analytical methods and external references. Additionally, significant variations in patient follow-up times were observed, with a P value of <.001 on the chi-square test, showing a stronger influence of experience on follow-up time decisions. The study highlights significant variations in the decision-making process of novice and expert optometrists in glaucoma diagnosis, with experience playing a key role in accuracy, approach, and management. These findings demonstrate the critical need for AI systems tailored to varying levels of expertise. They also provide insights for the future design of AI systems aimed at enhancing the diagnostic accuracy of optometrists and consistency across different expertise levels, ultimately improving patient outcomes in optometric practice.

We analyze how academic experts and nonexperts forecast the results of 15 piece-rate and behavioral treatments in a real-effort task. The average forecast of experts closely predicts the experimental results, with a strong wisdom-of-crowds effect: the average forecast outperforms 96 percent of individual forecasts. Citations, academic rank, field, and contextual experience do not correlate with accuracy. Experts as a group do better than nonexperts, but not if accuracy is defined as rank-ordering treatments. Measures of effort, confidence, and revealed ability are predictive of forecast accuracy to some extent and allow us to identify “superforecasters” among the nonexperts.

Responsible innovation in artificial intelligence (AI) calls for public deliberation: well-informed “deep democratic” debate that involves actors from the public, private, and civil society sectors in joint efforts to critically address the goals and means of AI. Adopting such an approach constitutes a challenge, however, due to the opacity of AI and strong knowledge boundaries between experts and citizens. This undermines trust in AI and undercuts key conditions for deliberation. We approach this challenge as a problem of situating the knowledge of actors from the AI industry within a deliberative system. We develop a new framework of responsibilities for AI innovation as well as a deliberative governance approach for enacting these responsibilities. In elucidating this approach, we show how actors from the AI industry can most effectively engage with experts and nonexperts in different social venues to facilitate well-informed judgments on opaque AI systems and thus effectuate their democratic governance.

Effectuation theory offers new ways of understanding entrepreneurial decisions and it is opposed to the traditional, rational, and so-called causal approach. A significant portion of the effectuation literature is rooted in the idea of entrepreneurial experience and expertise, with conclusions suggesting that novice and expert entrepreneurs apply effectual and causal logics differently when making decisions about their ventures. Further, resources are seen as critical to deployment of effectual vs. causal behaviors. The main goal of this paper is thus to evaluate whether the roles of effectuation and causation in performance differ for experts and novices, and how these logics interact with resource availability. Based on data from a sample composed of 178 new technology-based firms (NTBFs), our analyses show that causation seems to be determinant of firm performance in experts’ ventures only, whereas effectuation is relevant for both experts and novices. Moreover, the availability of resources seems to interact only with causation in their relationship to performance.

Visual expertise has been explored in numerous professions, but research on teachers' vision remains limited. Teachers' visual expertise is an important professional skill, particularly the ability to simultaneously perceive and interpret classroom situations for effective classroom management. This skill is complex and relies on an awareness of classroom events. Using eye tracking measurements and verbal think aloud, we investigated differences in how expert and novice teachers perceive problematic classroom scenes. Sixty-seven teachers participated, 35 experienced secondary school teachers (experts) and 32 teachers-in-training (novices). Participants viewed videos of authentic lessons and their eye movements were recorded as they verbalized thoughts about what they had seen in the lesson and how it was relevant to classroom management. Two different types of videos were viewed: lesson fragments showing (1) multiple events depicting disengaged students with no overt disruptions and (2) multiple events that included a prominent disruptive event affecting the class. Analysis of eye movements showed that novices' viewing was more dispersed whereas experts' was more focused. Irrespective of the video type, expert teachers focused their attention on areas where relevant information was available, while novice teachers' attention was more scattered across the classroom. Experts' perception appears to be more knowledge-driven whereas novices' appears more image-driven. Experts monitored more areas than novices, while novices skipped more areas than experts. Word usage also differed, showing that expertise was associated with a higher frequency of words referencing cognition, perception, and events than novices.

Digital farming technologies promise to help farmers make well-informed decisions that improve the quality and quantity of their production, with less labour and less impact on the environment. This future, however, can only become a reality if farmers are willing to share their data with agribusinesses that develop digital technologies. To foster trust in data sharing, in Europe the EU Code of Conduct for agricultural data sharing by contractual agreement was launched in 2018 which encourages transparency about data use. This article looks at the EU Code through the lens of literature on trust and contract agreements. We agree with the makers of the EU Code that a contract can make an important contribution to trust relationships as it is needed to mitigate the detrimental effects of power relationships between experts and non-experts. Building on Onora O’Neills perspective to trust, however, we argued that a contract can only be successful in fostering trust when (a) information is comprehended by the more vulnerable party in this relationship who has to sign the contract, (b) the more powerful partner takes responsibility to provide that information, and (c) information is tailored to the information needs of the party signing the contract, even when data are re-used over a longer period. In addition, we think that differences between trust relationships and relationships of accountability, give reason to add to informed consent other more substantive ethical components in a more encompassing code of conduct.

Shortages in the nursing profession are increasing. It is, therefore, imperative to understand why novice nurses are leaving the profession. This qualitative study explores Dutch novice nurses’ motives for leaving the profession. Individual semi-structured interviews were held with seventeen former novice nurses who had decided to leave nursing within two years after graduation. Data was collected and analysed following the principles of Thematic Analysis, leading to six themes; 1) Lack of challenge; ambitious to progress further in management or research roles. 2) Lack of passion; no feeling of passion for patient care. 3) Lack of perceived competence; not feeling “up to the challenge”. 4) Lack of job satisfaction due to heavy workload; work-life imbalance and inability to deliver high-quality care. 5) Lack of work capacity due to non-work-related health conditions; unmet requirements for job or work environment adjustment. 6) Lack of feeling of belonging; suffering from a negative attitude of colleagues to one another. To prevent novice nurse professional turnover, measures such as capacity building, supervisor support and a tailored personal development plan could be taken. To make novice nurses feel safe and reassured, support from colleagues and supervisors is important. Such measures require thoughtful implementation and evaluation. •This study addresses reasons for professional turnover of Dutch novice nurses.•Six core themes emerged as motives for leaving the profession in novice nurses.•No single motive for leaving the profession dominates.•To retain novice nurses, supervisor support and personal development are important.

Past research has suggested a fundamental principle of price precision: The more precise an opening price, the more it anchors counteroffers. The present research challenges this principle by demonstrating a too-much-precision effect. Five experiments (involving 1,320 experts and amateurs in real-estate, jewelry, car, and human-resources negotiations) showed that increasing the precision of an opening offer had positive linear effects for amateurs but inverted-U-shaped effects for experts. Anchor precision backfired because experts saw too much precision as reflecting a lack of competence. This negative effect held unless first movers gave rationales that boosted experts' perception of their competence. Statistical mediation and experimental moderation established the critical role of competence attributions. This research disentangles competing theoretical accounts (attribution of competence vs. scale granularity) and qualifies two putative truisms: that anchors affect experts and amateurs equally, and that more precise prices are linearly more potent anchors. The results refine current theoretical understanding of anchoring and have significant implications for everyday life.

Thoughtful, quiet, humble, prayerful, generous, dedicated, diligent, kind, friendly, loving--are just some of the descriptors that occur to me in reflecting on Sister Mary Elizabeth Gallagher. I first knew who Sister James Irene was when the 1951 entrance class was at the Mont preparing for final vows when we, the entrance class of 1958, were novices. Little did we know that many years later our paths would cross and merge in the education-related profession of librarianship. I tend to think that we all belong to a number of communities. For many of us here, the Community of St. Joseph is primary, for Mary was a vowed member, and served with our partners in ministry at our beloved Elms College.

In forensic ballistics, the perforation of glass by a bullet leaves characteristic marks, such as radial and concentric fractures and cones, which are crucial for reconstructing the bullet's trajectory and the shooting scene. Traditionally, digital forensic photography has been used to document these distinct characteristics. This research compared the ability of experts and trainees to accurately assess the directionality of bullets shot through vehicle windshields using photography and two innovative approaches: 1) paper and pencil to create a frottage and 2) latent print powder and gel lift. Two vehicle windshields were shot six times, from the outside and the inside, b two different types of ammunition. The inside and outside of eac bullet hole was documented via the three methods. The documentation tation for each bullet crater was blindly presented to experts and novices to assess their accuracy determining entry versus exit. The two innovative approaches of paper and pencil method and gel lift method produced higher accuracy for both experts and trainees when compared to traditional photography. Furthermore, there was no significant difference in accuracy of the experts and novices. This work aims to contribute to the ongoing refinement of forensic ballistics methodologies, enhancing the precision and reliability of evidence collection in the