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In both a field study ( N  = 28,064) and a preregistered vignette experiment ( N  = 800), we find that more perceived structure in evaluation processes is associated with higher levels of belonging for employees. More structured evaluation processes increase perceptions of procedural justice and of genuine commitment to diversity, equity, and inclusion—both of which are tied to a greater sense of belonging. Managerial status moderates this relationship: managers report a greater sense of belonging than non-managers in organizations with less structured evaluations, but the gap between managers’ and non-managers’ sense of belonging narrows in organizations with more structured evaluations. However, more structured evaluation processes also reduce managers’ feelings of autonomy, suggesting that more structure is not unequivocally better. Our findings highlight the importance of perceptions of fairness and of organizational commitment to diversity, equity, and inclusion efforts in driving employees’ sense of belonging. Practically, organizations may want to invest in more structured evaluation processes to boost sense of belonging, while recognizing that the benefits of doing so may be largest for non-managerial employees.

This paper proposes a model of learning-oriented assessment to inform assessment theory and practice. The model focuses on three interrelated processes: the assessment tasks which students undertake; students' development of self-evaluative capacities; and student engagement with feedback. These three strands are explored through the analysis of assessment practice in context. The research method involves in-depth classroom observations of five recipients of awards for teaching excellence across multiple disciplines; and semi-structured interviews with these teachers and a sample of their students. Findings highlight assessment tasks promoting thinking and practicing in the discipline; the use of critical reviews to develop student understandings of quality work; and 'same day feedback' to promote timely dialogues with students. The coherence of the model is discussed and some areas for further exploration are suggested. (HRK / Abstract übernommen).

Background Clinical education in Emergency services (EMS) is unique due to its dynamic environment, brief patient encounters, and unpredictable cases. EMS provides valuable learning opportunities for nursing students, fostering person-centered care approaches and a variation of clinical training and learning. Formative feedback is crucial to develop knowledge and skills. Multisource feedback (MSF) offers a comprehensive assessment by incorporating feedback from various individuals, promoting self-reflection and targeted learning. MSF has not, to our knowledge, been systematically evaluated in the context of EMS, and therefore, the aim of the study was to describe nursing students’ experiences with MSF during their clinical education in the EMS, using a digital instrument as a facilitating tool. Methods A qualitative design with an inductive approach was used. Data were collected in 2021, using focus group interviews ( n  = 4) with 31 final-semester nursing students in Stockholm, Sweden, who had conducted clinical education in the EMS and received MSF through a digital instrument. Data were analyzed using reflexive thematic analysis, guided by Braun and Clarke’s methodology. Results Three themes revealed: feedback from sources familiar with the student’s learning objectives, feedback from sources unfamiliar with the learning objectives, and general perceptions of MSF in the EMS. Students valued self-reflection and feedback from peers and supervisors for personal and professional growth. Patient feedback was challenging due to their limited contextual understanding and emotional states, while feedback from other healthcare professionals was appreciated but hindered by the healthcare professionals’ workload and timing constraints. Overall, students appreciated MSF’s diverse perspectives, enriching their learning, performance, and development. Conclusion This study underscores the value of MSF in nursing students’ clinical education within the EMS. Feedback from peers, supervisors, and self-reflection enhances self-awareness, professional growth, and mutual support. Despite challenges like stress and logistical barriers, structured support and a digital instrument improved accessibility and alignment with learning objectives for the students. Incorporating patient and healthcare professionals’ feedback enriches education by promoting patient-centred care and collaboration. MSF supported reflective practice, and team dynamics and highlights the need for refined feedback processes to optimize learning and professional development for nursing students during clinical education.

Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important steps in process mining. Many discovery techniques have been proposed, but none of them combines scalability with strong quality guarantees. We would like such techniques to handle billions of events or thousands of activities, to produce sound models (without deadlocks and other anomalies), and to guarantee that the underlying process can be rediscovered when sufficient information is available. In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framework. To measure the quality of discovered models for such large logs, we introduce a model–model and model–log comparison framework that applies a divide-and-conquer strategy to measure recall, fitness, and precision. We experimentally show that these discovery and measuring techniques sacrifice little compared to other algorithms, while gaining the ability to cope with event logs of 100,000,000 traces and processes of 10,000 activities on a standard computer.

Background Clinical decision-making (CDM) refers to physicians’ ability to gather, evaluate, and interpret relevant diagnostic information. An integral component of CDM is the medical history conversation, traditionally practiced on real or simulated patients. In this study, we explored the potential of using Large Language Models (LLM) to simulate patient-doctor interactions and provide structured feedback. Methods We developed AI prompts to simulate patients with different symptoms, engaging in realistic medical history conversations. In our double-blind randomized design, the control group participated in simulated medical history conversations with AI patients (control group), while the intervention group, in addition to simulated conversations, also received AI-generated feedback on their performances (feedback group). We examined the influence of feedback based on their CDM performance, which was evaluated by two raters (ICC = 0.924) using the Clinical Reasoning Indicator – History Taking Inventory (CRI-HTI). The data was analyzed using an ANOVA for repeated measures. Results Our final sample included 21 medical students (age mean = 22.10 years, semester mean = 4, 14 females). At baseline, the feedback group (mean = 3.28 ± 0.09 [standard deviation]) and the control group (3.21 ± 0.08) achieved similar CRI-HTI scores, indicating successful randomization. After only four training sessions, the feedback group (3.60 ± 0.13) outperformed the control group (3.02 ± 0.12), F (1,18) = 4.44, p  = .049 with a strong effect size, partial η 2  = 0.198. Specifically, the feedback group showed improvements in the subdomains of CDM of creating context ( p  = .046) and securing information ( p  = .018), while their ability to focus questions did not improve significantly ( p  = .265). Conclusion The results suggest that AI-simulated medical history conversations can support CDM training, especially when combined with structured feedback. Such training format may serve as a cost-effective supplement to existing training methods, better preparing students for real medical history conversations.

A distinction is often made in the literature about \"assessment of learning\" and \"assessment for learning\" attributing a formative function to the latter while the former takes a summative function. While there may be disagreements among researchers and educators about such categorical distinctions there is consensus that both types of assessment are often used concurrently in higher education institutions. A question that often arises when formative and summative assessment practices are used in continuous assessment is the extent to which student learning can be facilitated through feedback. The views and perceptions of students and academics from a discipline in the Humanities across seven higher education institutions were sought to examine the above question. A postal survey was completed by academics, along with a survey administered to a sample of undergraduate students and a semi-structured interview was conducted with key academics in each of the seven institutions. This comparative study highlights issues that concern both groups about the extent to which continuous assessment practices facilitate student learning and the challenges faced. The findings illustrate the need to consider more effective and efficient ways in which feedback can be better used to facilitate student learning. (HRK / Abstract übernommen).

Background Health systems science (HSS) education is an increasingly important component of undergraduate medical education. Despite curricular advances, the ways in which clinicians implement health systems science knowledge in everyday clinical practice, health systems thinking, remains understudied. A better understanding of how clinicians engage in health systems thinking to address everyday problems in clinical contexts is needed. Methods We conducted semi-structured interviews with 10 expert clinicians experienced in undergraduate medical education, health systems science, and curriculum development to identify components of competent health systems thinking. Interview questions were informed by ecological systems theory and literature on learning professional competencies. Results Through interviews with experts, we have come to define health systems thinking (HST) as “an approach to solving problems in healthcare systems that utilizes a deeper understanding of interconnections and behavior of the entire system. As a skill, it coordinates the application of clinical and HSS knowledge and skills toward solving a contextual problem in the healthcare environment.” Clinician comments support the idea that HST is a metacognitive process rather than a specific subset of knowledge domains or affective attributes. This process requires that clinicians understand and navigate pressures on patient care originating from surrounding meso- and macro-systems. Conclusions Medical students require more explicit exposure to HSS knowledge being implemented in clinical environments, and varied examples highlighting how meso- and macro-system patterns can impact individual patient care. This metacognitive integration of HSS knowledge into everyday clinical practice is critical for preparing medical students to meet the requirements of the accreditation council for graduate medical education (ACGME) core competencies in residency programs. Health systems thinking requires a method of operational assessment to provide students feedback and highlight targeted interventions for further development.

This study investigates the role of self-assessment (SA), classroom climate (CC), and psychological variables in learning-oriented assessment (LOA) within English as a foreign language (EFL) education. Utilizing a mixed-methods approach, data was collected through surveys and semi-structured interviews from EFL learners and instructors. Findings reveal that SA plays a pivotal role in promoting learner autonomy, self-regulated learning, and academic engagement (AE). By actively involving students in the assessment process and providing opportunities for reflection and feedback, educators can empower learners to take ownership of their learning journey and enhance their overall learning outcomes. Moreover, the study underscores the importance of creating a supportive CC characterized by mutual respect, collaboration, and inclusivity. Positive teacher-student relationships and inclusive learning environments contribute significantly to students’ academic and socioemotional development. Additionally, the study highlights the influence of psychological variables such as growth mindset and emotional intelligence (EI) on student learning outcomes. Learners who adopt a growth-oriented mindset and possess high levels of EI are more likely to persevere in the face of challenges and actively engage in the learning process. The implications of this study suggest the importance of adopting a holistic approach to assessment and instruction in EFL education, with stakeholders urged to incorporate strategies to promote SA, create supportive CC, and foster psychological variables for enhanced student learning and well-being.

The combination of measuring performance and giving feedback creates tension between formative and summative purposes of progress evaluations and can be challenging for supervisors. There are conflicting perspectives and evidence on the effects supervisor-trainee relationships have on assessing performance. The aim of this study was to learn how progress evaluations are used in postgraduate education with longitudinal supervisor-trainee relationships. Progress evaluations in a two-year community-pharmacy specialization program were studied with a mixed-method approach. An adapted version of the Canadian Medical Education Directives for Specialists (CanMEDS) framework was used. Validity of the performance evaluation scores of 342 trainees was analyzed using repeated measures ANOVA. Semi-structured interviews were held with fifteen supervisors to investigate their response processes, the utility of the progress evaluations, and the influence of supervisor-trainee relationships. Time and CanMEDS roles affected the three-monthly progress evaluation scores. Interviews revealed that supervisors varied in their response processes. They were more committed to stimulating development than to scoring actual performance. Progress evaluations were utilized to discuss and give feedback on trainee development and to add structure to the learning process. A positive supervisor-trainee relationship was seen as the foundation for feedback and supervisors preferred the roles of educator, mentor, and coach over the role of assessor. We found that progress evaluations are a good method for directing feedback in longitudinal supervisor-trainee relationships. The reliability of scoring performance was low. We recommend progress evaluations to be independent of formal assessments in order to minimize roles-conflicts of supervisors.

This paper discusses a large-scale dockless bike-sharing system (DBSS) with unusable bikes, which can be removed, repaired, redistributed and reused under two batch policies: one for removing the unusable bikes from each parking region to a maintenance shop, and the other for redistributing the repaired bikes from the maintenance shop to some suitable parking regions. For such a bike-sharing system, this paper proposes and develops a new computational method by applying the RG-factorizations of block-structured Markov processes to the closed queueing networks. Different from previous works in the literature of queueing networks, a key contribution of our computational method is to set up a new nonlinear matrix equation to determine the relative arrival rates, and to show that the nonlinearity comes from two different groups of processes: The failure and removing processes, and the repair and redistribution processes. Once the relative arrival rate is introduced to each node, these nodes are isolated from each other so that the Markov processes of all the nodes are independent of each other, and thus the Markov system of each node is described as an elegant block-structured Markov process whose stationary probabilities can be easily computed by the RG-factorizations. Based on this, the paper establishes a more general product-form solution of the closed queueing network, and provides performance analysis of the DBSS through a comprehensive discussion of the bikes’ failure, removing, repair, redistribution and reuse processes under two batch policies. We hope that our method opens a new avenue for quantitative evaluation of more general DBSSs with unusable bikes.