Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information.

Emerging studies have shown that circular RNAs (circRNAs) are involved in a variety of biological processes and play a key role in disease diagnosing, treating and inferring. Although many methods, including traditional machine learning and deep learning, have been developed to predict associations between circRNAs and diseases, the biological function of circRNAs has not been fully exploited. Some methods have explored disease-related circRNAs based on different views, but how to efficiently use the multi-view data about circRNA is still not well studied. Therefore, we propose a computational model to predict potential circRNA-disease associations based on collaborative learning with circRNA multi-view functional annotations. First, we extract circRNA multi-view functional annotations and build circRNA association networks, respectively, to enable effective network fusion. Then, a collaborative deep learning framework for multi-view information is designed to get circRNA multi-source information features, which can make full use of the internal relationship among circRNA multi-view information. We build a network consisting of circRNAs and diseases by their functional similarity and extract the consistency description information of circRNAs and diseases. Last, we predict potential associations between circRNAs and diseases based on graph auto encoder. Our computational model has better performance in predicting candidate disease-related circRNAs than the existing ones. Furthermore, it shows the high practicability of the method that we use several common diseases as case studies to find some unknown circRNAs related to them. The experiments show that CLCDA can efficiently predict disease-related circRNAs and are helpful for the diagnosis and treatment of human disease.

Synthesis and Assembly of Virtual Collaborations.

The interplay between academics and society within the environment of the COVID-19 pandemic has impacted on scientists across the world, prompting reevaluation of how virtual toolboxes can be used to support responsible collaborative research practices. We provide awareness of virtual resources and activities that enable scientific discovery using safe and efficient practices.

Addressing multi-site functional MRI heterogeneity through dual-expert collaborative learning for brain disease identification.

Several studies employ multi-site rs-fMRI data for major depressive disorder (MDD) identification, with a specific site as the to-be-analyzed target domain and other site(s) as the source domain. But they usually suffer from significant inter-site heterogeneity caused by the use of different scanners and/or scanning protocols and fail to build generalizable models that can well adapt to multiple target domains. In this article, we propose a dual-expert fMRI harmonization (DFH) framework for automated MDD diagnosis. Our DFH is designed to simultaneously exploit data from a single labeled source domain/site and two unlabeled target domains for mitigating data distribution differences across domains. Specifically, the DFH consists of a domain-generic student model and two domain-specific teacher/expert models that are jointly trained to perform knowledge distillation through a deep collaborative learning module. A student model with strong generalizability is finally derived, which can be well adapted to unseen target domains and analysis of other brain diseases. To the best of our knowledge, this is among the first attempts to investigate multi-target fMRI harmonization for MDD diagnosis. Comprehensive experiments on 836 subjects with rs-fMRI data from 3 different sites show the superiority of our method. The discriminative brain functional connectivities identified by our method could be regarded as potential biomarkers for fMRI-related MDD diagnosis.

Getting real in interprofessional clinical placements: patient-centeredness in student teams' collaborative learning.

Collaboration between healthcare providers helps tackle the increasing complexity of healthcare. When learning teamwork, interprofessional students are expected to work patient-centered; recognizing the patient's expertise and partnering with them. Research on interprofessional education (IPE) for undergraduates has illuminated learning outcomes, organization of learning activities, change in attitudes, etc. But, we know little about the interaction between patients and interprofessional student teams. This study aimed to explore how interprofessional student teams and patients interact in interprofessional clinical placements. With a focused ethnographic approach, participant observation and qualitative interviews were conducted in two contexts; a physical and an online arrangement. Central ideas in Goffman's dramaturgy constituted a theoretical lens. A reflexive thematic analysis generated three themes: (1) Preparing safe and comfortable encounters with patients, (2) Including and excluding the patient in the encounter, and (3) Adjusting to the patient's situation. We identified students' intentions of patient-centeredness when preparing encounters, but patients did not always feel included and listened to in encounters. After encountering patients, student teams adjusted their teamwork, by changing the team composition or the planned clinical interventions to better meet the patients' needs. Notably, team-based patient encounters led to a different view of the patient, their health issues, and how to collaborate. Our findings can inform educators of the importance of addressing patient-centered care in interprofessional learning arrangements. Today, clinical interprofessional placements may not exploit the potential for learning about patient-centeredness. A thematization of this, e.g., in supervision in future clinical placements can ensure an enhanced focus on this in interprofessional teamwork.

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds.

The quantitative prediction of human pharmacokinetics (PK) including the PK profile and key PK parameters are critical for early drug development decisions, successful phase I clinical trials, and the establishment of a range of doses to enable phase II clinical dose selection. Here, we describe an approach employing physiologically based pharmacokinetic (PBPK) modeling (Simcyp) to predict human PK and to validate its performance through retrospective analysis of 18 Genentech compounds for which clinical data are available. In short, physicochemical parameters and in vitro data for preclinical species were integrated using PBPK modeling to predict the in vivo PK observed in mouse, rat, dog, and cynomolgus monkey. Through this process, the in vitro to in vivo extrapolation (IVIVE) was determined and then incorporated into PBPK modeling in order to predict human PK. Overall, the prediction obtained using this PBPK-IVIVE approach captured the observed human PK profiles of the compounds from the dataset well. The predicted Cmax was within 2-fold of the observed Cmax for 94% of the compounds while the predicted area under the curve (AUC) was within 2-fold of the observed AUC for 72% of the compounds. Additionally, important IVIVE trends were revealed through this investigation, including application of scaling factors determined from preclinical IVIVE to human PK prediction for each molecule. Based upon the analysis, this PBPK-based approach now serves as a practical strategy for human PK prediction at the candidate selection stage at Genentech.

Enhancing collaborative learning in health management education: an investigation of Padlet-mediated interventions and the influence of flexible thinking.

This study had three primary goals. First, it aimed to craft an intervention program centered around collaborative learning enabled by Padlet. Second, it aimed to gauge the perceptions of health management students regarding this intervention and how it affected their collaborative learning experiences. Additionally, the third objective of the study aimed to investigate how students' flexible thinking within the learning process might shape their perceptions of the advantages derived from this instructional activity within the domain of online collaborative learning. Data for the analysis were gathered from 100 Israeli undergraduate students by two measurements: Flexible thinking in learning and Student perceptions of collaborative learning via Padlet. The intervention program included several stages. First, the students discussed the pedagogic objective of using Padlet. In the second stage, the students were presented with ill-structured problems related to the course content. Each group had to choose one problem and analyze it from three perspectives discussed in the course-healthcare provider, patient, and organization. Next, the students presented and explained their solutions employing the shared knowledge base. The final work was presented in different formats using various technologies. The PLS-SEM analysis has corroborated our hypothesis that students' flexible thinking might positively contribute to their perception of Padlet utilization. According to the empirical model, in general, students who perceived themselves as more flexible were found more receptive to utilizing the proposed technological tool (Padlet) and hence tended to appreciate its function as a collaborative learning platform enabler. This study mainly underscores the important role flexible thinking plays in motivating managers and medical professionals to embrace innovative technologies or methods for teamwork, that could enable them to weigh arguments, seek alternative solutions to authentic problems, and adjust their approaches effectively and collaboratively as new challenges emerge.

A Federated Attention-Based Multimodal Biometric Recognition Approach in IoT.

The rise of artificial intelligence applications has led to a surge in Internet of Things (IoT) research. Biometric recognition methods are extensively used in IoT access control due to their convenience. To address the limitations of unimodal biometric recognition systems, we propose an attention-based multimodal biometric recognition (AMBR) network that incorporates attention mechanisms to extract biometric features and fuse the modalities effectively. Additionally, to overcome issues of data privacy and regulation associated with collecting training data in IoT systems, we utilize Federated Learning (FL) to train our model This collaborative machine-learning approach enables data parties to train models while preserving data privacy. Our proposed approach achieves 0.68%, 0.47%, and 0.80% Equal Error Rate (EER) on the three VoxCeleb1 official trial lists, performs favorably against the current methods, and the experimental results in FL settings illustrate the potential of AMBR with an FL approach in the multimodal biometric recognition scenario.

Improving Active Collaborative Clinical Learning Through a Mobile Application for Undergraduate Nursing Students.

Innovative teaching and learning strategies in pediatric clinical education are needed to ensure that learning outcomes are achieved efficiently and to improve students' satisfaction with the learning process. We developed a mobile application to enhance the clinical learning of nursing students during pediatric clinical practice and explored the participants' perceptions of the learning method. A mixed-method design and an online survey were used to assess participants' perceived achievement of learning objectives and satisfaction with the clinical practicum. Focus groups explored the participants' experience of using the application. The overall achievement of learning outcomes was 4.1 out of 5, and participants reported high satisfaction with the clinical practicum using the mobile application. Qualitative analysis of the transcripts revealed four major themes: (1) facilitation of collaborative learning through real-time interactions and communication; (2) engagement in active learning through an application; (3) a useful tool for enhancing critical thinking; and (4) the "curate's egg" nature of the application. The mobile application can be incorporated into the clinical education of nursing students to promote self-regulated learning and collaboration with faculty. Clear guidance on how to use the application in a clinical environment should be provided to students and preceptors to achieve better learning outcomes.

Bugs, Bumps, and Bacteria: The Role of Virtual Collaborative Learning Network in Pediatric Dermatology.

Patients in rural and underserved areas face significant barriers in accessing specialty care due to unavailability of services, geographic isolation, travel burden, and other cultural and socioeconomic factors.1 Pediatric dermatology is among the top three subspecialties that provides routine care for pediatric patients, however, shortage and maldistribution of pediatric dermatologists have remained a major hurdle for those living in remote and isolated areas.2 Pediatric dermatologists cluster in urban areas with high-patient volume and estimated wait times for new patients that often exceed 13 weeks, making access one of the major drivers of inequity for rural patients.2-4.

Improving autonomy support: a collaborative learning experience / Améliorer les pratiques d'accompagnement à l'autonomie en santé : effets d'un dispositif d'apprentissage coopératif

Introduction: The ACESO project, which was part of the Autonomy support in health national experimentation, brought together 21 partners from Ile-de-France. Among these partners, 14 had practices similar to autonomy support. Partners' presupposition was that experimenting a cooperative approach would encourage the empowerment of participants, improve their autonomy support and put into place the conditions necessary for the empowerment of people who would be supported. To help participants to meet this goal, the project leader took on a role as third party whose function was to facilitate the cooperative approach by proposing a framework and a method. Purpose of research: The study aimed to report the effects of this approach on the participants' practices as well as to identify the process for achieving this. Results: The participants' learning enabled them to align themselves with the good practice guidelines collectively constructed within the project. With the project leader's support, they initiated a transformative learning process that allowed them to develop their reflexivity and empowerment. These transformations had repercussions on their teams and structures, through a halo effect. The halo effect varied, in each partner structure, according to the participation and involvement of the project referent and the other members of the structure, in particular managers. Conclusions: This study has highlighted the value of a cooperative approach to facilitate the learning necessary for sustainable practices transformations and the improvement partners autonomy supports. This resulted in gains in autonomy for the autonomy support practitioners and the people they supported.

Introduction: Le projet ACESO, participant à l'expérimentation nationale des dispositifs d'accompagnement à l'autonomie en santé (AAS), a rassemblé 21 partenaires franciliens parmi lesquels 14 portaient des pratiques qui empruntent à l'accompagnement. Son présupposé était qu'en expérimentant une démarche coopérative favorisant l'empowerment des partenaires, ceux-ci amélioreraient leurs pratiques d'accompagnement et mettraient notamment en place les conditions nécessaires à l'empowerment des personnes accompagnées. Pour les y aider, le porteur de projet a tenu un rôle de tiers dont la fonction était de faciliter la démarche coopérative en proposant un cadre et une méthode. But de l'étude: L'étude visait à rendre compte des effets de cette démarche sur les pratiques des partenaires, ainsi qu'à identifier le processus pour y parvenir. Résultats: Les apprentissages réalisés ont permis aux partenaires de se donner des balises de bonnes pratiques construites collectivement au sein du projet (valeurs, principes et postures). Avec le soutien du tiers, ils ont initié un processus d'apprentissage transformationnel développant leur réflexivité et leur empowerment. Ces transformations ont eu des répercussions sur leurs équipes et structures, par effet de halo. Ce dernier a varié, dans chaque structure partenaire, en fonction de la participation et de l'implication du référent-projet et des membres de la structure, en particulier la direction. Conclusion: Cette étude met en évidence l'intérêt d'une démarche coopérative pour faciliter l'apprentissage nécessaire aux transformations durables des pratiques et l'amélioration des pratiques de partenaires d'un collectif apprenant. Dans le cas de l'AAS, ceci s'est traduit par des gains d'autonomie pour les accompagnants et les personnes accompagnées.

Privacy preserving collaborative learning of generalized linear mixed model.

Generalized Linear Mixed Model is one of the most pervasive class of statistical models. It is widely used in the medical domain. Training such models in a collaborative setting often entails privacy risks. Standard privacy preserving mechanisms such as differential privacy can be used to mitigate the privacy risk during training the model. However, experimental evidence suggests that adding differential privacy to the training of the model can cause significant utility loss which makes the model impractical for real world usage. Therefore, it becomes clear that the specific class of generalized linear mixed models which lose their usability under differential privacy requires a different approach for privacy preserving model training. In this work, we propose a value-blind training method in a collaborative setting for generalized linear mixed models. In our proposed training method, the central server optimizes model parameters for a generalized linear mixed model without ever getting access to the raw training data or intermediate computation values. Intermediate computation values that are shared by the collaborating parties with the central server are encrypted using homomorphic encryption. Experimentation on multiple datasets suggests that the model trained by our proposed method achieves very low error rate while preserving privacy. To the best of our knowledge, this is the first work that performs a systematic privacy analysis of generalized linear mixed model training in collaborative setting.

The Hebb repetition effect in complex span tasks: Evidence for a shared learning mechanism with simple span tasks.

The Hebb repetition effect on serial-recall task refers to the improvement in the accuracy of recall of a repeated list (e.g., repeated in every 3 trials) over random non-repeated lists. Previous research has shown that both temporal position and neighboring items need to be the same on each repetition list for the Hebb repetition effect to occur, suggesting chunking as one of its underlying mechanisms. Accordingly, one can expect absence of the Hebb repetition effect in a complex span task, given that the sequence is interrupted by distractors. Nevertheless, one study by Oberauer, Jones, and Lewandowsky (2015, Memory & Cognition, 43[6], 852-865) showed evidence of the Hebb repetition effect in a complex span task. Throughout four experiments, we confirmed the Hebb repetition effect in complex span tasks, even when we included distractors in both encoding and recall phases to avoid any resemblance to a simple span task and minimized the possibility of chunking. Results showed that the Hebb repetition effect was not affected by the distractors during encoding and recall. A transfer cycle analysis showed that the long-term knowledge acquired in the complex span task can be transferred to a simple span task. These findings provide the first insights on the mechanism behind the Hebb repetition effect in complex span tasks; it is at least partially based on the same mechanism that improves recall performance by repetition in simple span tasks.

Learning with friends and strangers: partner familiarity does not improve collaborative learning performance in younger and older adults.

Collaborative learning with a familiar partner can reduce age-related differences in learning and memory compared to learning alone. This study compares younger and older adults' learning with familiar and unfamiliar partners to determine whether familiarity is beneficial for collaborative learning. Twenty-four younger adults aged 18-28 years and 24 older adults aged 60-80 years participated in familiar and unfamiliar pairs. Participants were asked to arrange abstract tangram shapes in a specific order on a grid over multiple trials; the directors' tangram cards were arranged in a specific order on the grid and this order was communicated to the matcher. Older adults initially took longer to complete the task, using more words to correctly arrange the tangrams. Over multiple trials, a learning effect was observed in both groups, although older adults did not perform with similar efficiency to younger adults. Familiarity had no effect on performance. These findings suggest that the familiarity of a partner does not affect learning outcomes in younger or older adults when learning in a social context. Collaborative learning may be beneficial for older adults, even if they do not know their learning partner, which may have implications for adult education and lifelong learning.

The role of collaborative learning in resilience in healthcare-a thematic qualitative meta-synthesis of resilience narratives.

BACKGROUND: To provide high quality services in increasingly complex, constantly changing circumstances, healthcare organizations worldwide need a high level of resilience, to adapt and respond to challenges and changes at all system levels. For healthcare organizations to strengthen their resilience, a significant level of continuous learning is required. Given the interdependence required amongst healthcare professionals and stakeholders when providing healthcare, this learning needs to be collaborative, as a prerequisite to operationalizing resilience in healthcare. As particular elements of collaborative working, and learning are likely to promote resilience, there is a need to explore the underlying collaborative learning mechanisms and how and why collaborations occur during adaptations and responses. The aim of this study is to describe collaborative learning processes in relation to resilient healthcare based on an investigation of narratives developed from studies representing diverse healthcare contexts and levels. METHODS: The method used to develop understanding of collaborative learning across diverse healthcare contexts and levels was to first conduct a narrative inquiry of a comprehensive dataset of published health services research studies. This resulted in 14 narratives (70 pages), synthesised from a total of 40 published articles and 6 PhD synopses. The narratives where then analysed using a thematic meta-synthesis approach. RESULTS: The results show that, across levels and contexts, healthcare professionals collaborate to respond and adapt to change, maintain processes and functions, and improve quality and safety. This collaboration comprises activities and interactions such as exchanging information, coordinating, negotiating, and aligning needs and developing buffers. The learning activities embedded in these collaborations are both activities of daily work, such as discussions, prioritizing and delegation of tasks, and intentional educational activities such as seminars or simulation activities. CONCLUSIONS: Based on these findings, we propose that the enactment of resilience in healthcare is dependent on these collaborations and learning processes, across different levels and contexts. A systems perspective of resilience demands collaboration and learning within and across all system levels. Creating space for reflection and awareness through activities of everyday work, could support individual, team and organizational learning.

Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments.

In the information age, the ability to read and construct data visualizations becomes as important as the ability to read and write text. However, while standard definitions and theoretical frameworks to teach and assess textual, mathematical, and visual literacy exist, current data visualization literacy (DVL) definitions and frameworks are not comprehensive enough to guide the design of DVL teaching and assessment. This paper introduces a data visualization literacy framework (DVL-FW) that was specifically developed to define, teach, and assess DVL. The holistic DVL-FW promotes both the reading and construction of data visualizations, a pairing analogous to that of both reading and writing in textual literacy and understanding and applying in mathematical literacy. Specifically, the DVL-FW defines a hierarchical typology of core concepts and details the process steps that are required to extract insights from data. Advancing the state of the art, the DVL-FW interlinks theoretical and procedural knowledge and showcases how both can be combined to design curricula and assessment measures for DVL. Earlier versions of the DVL-FW have been used to teach DVL to more than 8,500 residential and online students, and results from this effort have helped revise and validate the DVL-FW presented here.

Enabling creative collaboration for all levels of learning.

A potential path for enabling greater creativity and collaboration is through increased arts and science, technology, engineering, and mathematics (STEM) integration in education and research. This approach has been a growing discussion in US national forums and is the foundation of the science, technology, engineering, and mathematics plus arts and design (STEAM) education movement. Developing authentic artistic integrations with STEM fields (or vice versa) is challenging, particularly in higher education, where traditional disciplinary structures and incentives can impede the creation of integrated programs. Measuring and assessing the outcomes of such integration efforts can be even more challenging, since traditional metrics do not necessarily capture new opportunities created for students and faculty, and the greatest impact may occur over a long period (a career). At Drexel University, we created the Expressive & Creative Interaction Technologies (ExCITe) Center as a standalone institute to pursue and enable such transdisciplinary arts-STEM collaborations, particularly with external arts and education partners. In this perspectives paper, we highlight a range of projects and outcomes resulting from such external collaborations, including graduate research with professional artists, undergraduate student work experiences, and STEAM-based education programs for kindergarten through 12th-grade (K-12) students. While each project has its own specific objectives and outcomes, we believe that they collectively demonstrate this integrated transdisciplinary approach to be impactful and potentially transformative for all levels of learning.

Twelve tips for engaging learners in online discussions.

Educators play a critical role in designing, facilitating and delivering an online medical education experience. Their teaching decisions and practices shape learners' experiences and affect their achievements. This is true even in large-scale, open educational contexts such as Massive Open Online Courses (MOOCs), which are designed based on self-regulated and collaborative learning. Thus, it is particularly important to explore the ways in which educators can effectively support online or blended learning through their interactions with learners. The 12 tips in this article aim to provide medical educators with guidelines for creating engaging online discussions that both support and challenge learners' understanding. The advice draws on a recent study examining the experiences and activities of 24 educators in MOOC discussion areas, and how their learners engaged with them. It provides practical recommendations on facilitating online discussions, producing engaging discussion tasks, and creating a balanced educator presence.

Perceived positive social interdependence in online versus face-to-face team-based learning styles of collaborative learning: a randomized, controlled, mixed-methods study.

BACKGROUND: Collaborative learning is a group learning approach in which positive social interdependence within a group is key to better learning performance and future attitudes toward team practice. Recent attempts to replace a face-to-face environment with an online one have been developed using information communication technology. However, this raises the concern that online collaborative learning (OCL) may reduce positive social interdependence. Therefore, this study aimed to compare the degree of social interdependence in OCL with face-to-face environments and clarify aspects that affect social interdependence in OCL. METHODS: We conducted a crossover study comparing online and face-to-face collaborative learning environments in a clinical reasoning class using team-based learning for medical students (n = 124) in 2021. The participants were randomly assigned to two cohorts: Cohort A began in an online environment, while Cohort B began in a face-to-face environment. At the study's midpoint, the two cohorts exchanged the environments as a washout. The participants completed surveys using the social interdependence in collaborative learning scale (SOCS) to measure their perceived positive social interdependence before and after the class. Changes in the mean SOCS scores were compared using paired t-tests. Qualitative data related to the characteristics of the online environment were obtained from the focus groups and coded using thematic analysis. RESULTS: The matched-pair tests of SOCS showed significant progression between pre- and post-program scores in the online and face-to-face groups. There were no significant differences in overall SOCS scores between the two groups. Sub-analysis by subcategory showed significant improvement in boundary (discontinuities among individuals) and means interdependence (resources, roles, and tasks) in both groups, but outcome interdependence (goals and rewards) improved significantly only in the online group. Qualitative analysis revealed four major themes affecting social interdependence in OCL: communication, task-sharing process, perception of other groups, and working facilities. CONCLUSIONS: There is a difference in the communication styles of students in face-to-face and online environments, and these various influences equalize the social interdependence in a face-to-face and online environment.

Collaborative case-based learning with programmatic team-based assessment: a novel methodology for developing advanced skills in early-years medical students.

BACKGROUND: Imperial College London launched a new, spiral undergraduate medical curriculum in September 2019. Clinical & Scientific Integrative cases (CSI) is an innovative, flagship module, which uses pioneering methodology to provide early-years learning that [1] is patient-centred, [2] integrates clinical and scientific curriculum content, [3] develops advanced team-work skills and [4] provides engaging, student-driven learning. These aims are designed to produce medical graduates equipped to excel in a modern healthcare environment. METHODS: CSI has adopted a novel educational approach which utilises contemporary digital resources to deliver a collaborative case-based learning (CBL) component, paired with a team-based learning (TBL) component that incorporates both learning and programmatic assessment. This paper serves to explore how first-year students experienced CSI in relation to its key aims, drawing upon quantitative and qualitative data from feedback surveys from CSI's inaugural year. It provides a description and analysis of the module's design, delivery, successes and challenges. RESULTS: Our findings indicate that CSI has been extremely well-received and that the majority of students agree that it met its aims. Survey outputs indicate success in integrating multiple elements of the curriculum, developing an early holistic approach towards patients, expediting the development of important team-working skills, and delivering authentic and challenging clinical problems, which our students found highly relevant. Challenges have included supporting students to adapt to a student-driven, deep learning approach. CONCLUSIONS: First-year students appear to have adopted a patient-centred outlook, the ability to integrate knowledge from across the curriculum, an appreciation for other team members and the self-efficacy to collaboratively tackle challenging, authentic clinical problems. Ultimately, CSI's innovative design is attractive and pertinent to the needs of modern medical students and ultimately, future doctors.

Developing new possibilities for interprofessional learning- students' experience of learning together in the ambulance service.

BACKGROUND: It is known that setting and context matters, and contextual factors influence interprofessional education (IPE). Activities developed in a new setting should therefore be evaluated to determine students' experiences and learning. IPE in the ambulance service may present a new setting for interprofessional learning (IPL). AIM: The aim of this study was to explore undergraduate students' experiences of collaboration and learning together during their clinical rotation in the ambulance service. STUDY DESIGN AND METHOD: A mixed convergent parallel design was used to describe nursing and medical students' experiences of collaboration and learning together during their clinical rotation in the ambulance service during autumn 2019. Two group interviews with nursing students (n = 20; response rate 80%) were conducted and the medical students (n = 40; response rate 72.5%) answered a self-assessment questionnaire regarding their IPE. The group discussions were analysed using an inductive thematic analysis and descriptive statistics were used to describe the medical students' self-assessed experiences and competencies in interprofessional collaboration. RESULTS: In the context of the ambulance service, some of the challenges included, the team vary daily, a context that can be unpredictable, and the team being required to make decisions in various situations with limited support. The context presented good opportunities to learn together, since they faced a broad variety of situations and had opportunities to follow patients through the chain of care. CONCLUSION: The students' experiences show that the ambulance service offers possibilities for IPL. The ambulance service enhanced the students' learning in an unfamiliar environment, encouraging them to develop collaborative learning strategies and situational leadership regardless of established hierarchical structures and stereotypes that are sometimes present in other parts of the health care service. ETHICAL APPROVAL: By the Swedish Ethical Review Authority. No: 2019-03595.