[Breast cancer lesion segmentation based on co-learning feature fusion and Transformer].

The PET/CT imaging technology combining positron emission tomography (PET) and computed tomography (CT) is the most advanced imaging examination method currently, and is mainly used for tumor screening, differential diagnosis of benign and malignant tumors, staging and grading. This paper proposes a method for breast cancer lesion segmentation based on PET/CT bimodal images, and designs a dual-path U-Net framework, which mainly includes three modules: encoder module, feature fusion module and decoder module. Among them, the encoder module uses traditional convolution for feature extraction of single mode image; The feature fusion module adopts collaborative learning feature fusion technology and uses Transformer to extract the global features of the fusion image; The decoder module mainly uses multi-layer perceptron to achieve lesion segmentation. This experiment uses actual clinical PET/CT data to evaluate the effectiveness of the algorithm. The experimental results show that the accuracy, recall and accuracy of breast cancer lesion segmentation are 95.67%, 97.58% and 96.16%, respectively, which are better than the baseline algorithm. Therefore, it proves the rationality of the single and bimodal feature extraction method combining convolution and Transformer in the experimental design of this article, and provides reference for feature extraction methods for tasks such as multimodal medical image segmentation or classification.

Co-Learning Computational and Design Thinking Using Educational Robotics: A Case of Primary School Learners in Namibia.

In a two-day educational robotics workshop in a Namibian primary boarding school, learners with no programming skills managed to apply both computational and design thinking skills with the aid of educational robotics. Educational robotics has proved to be an area which enhances learning both computational thinking and design thinking. An educational robotics (ER) workshop focusing on Arduino robotics technologies was conducted with primary school learners at Nakayale Private Academy. Observation methods through watching, listening and video recordings were used to observe and analyze how the learners were interacting throughout the workshop. Based on the results, it was concluded that this approach could be applied in classrooms to enable the primary school learners apply computational and design thinking in preparation of becoming the producers and not only the consumers of the 4IR technologies.

Basket of options: Unpacking the concept.

How to stimulate technological change to enhance agricultural productivity and reduce poverty remains an area of vigorous debate. In the face of heterogeneity among farm households and rural areas, one proposition is to offer potential users a 'basket of options' - a range of agricultural technologies from which potential users may select the ones that are best suited to their specific circumstances. While the idea of a basket of options is now generally accepted, it has attracted little critical attention. In this paper, we reflect on outstanding questions: the appropriate dimensions of a basket, its contents and how they are identified, and how a basket might be presented. We conceive a basket of options in terms of its depth (number of options related to a problem or opportunity) and breadth (the number of different problems or opportunities addressed). The dimensions of a basket should reflect the framing of the problem or opportunity at hand and the objective in offering the basket. We recognise that increasing the number of options leads to a trade-off by decreasing the fraction of those options that are relevant to an individual user. Farmers might try out, adapt or use one or more of the options in a basket, possibly leading to a process of technological change. We emphasise that the selection (or not) of specific options from the basket, and potential adaptation of the options, provide important opportunities for learning. Baskets of options can therefore be understood as important boundary concepts that invite critical engagement, comparison and discussion. Significant knowledge gaps remain, however, about the best ways to present the basket and to guide potential users to select the options that are most relevant to them.

Community scientist program provides bi-directional communication and co-learning between researchers and community members.

Community involvement in research is key to translating science into practice, and new approaches to engaging community members in research design and implementation are needed. The Community Scientist Program, established at the MD Anderson Cancer Center in Houston in 2018 and expanded to two other Texas institutions in 2021, provides researchers with rapid feedback from community members on study feasibility and design, cultural appropriateness, participant recruitment, and research implementation. This paper aims to describe the Community Scientist Program and assess Community Scientists' and researchers' satisfaction with the program. We present the analysis of the data collected from 116 Community Scientists and 64 researchers who attended 100 feedback sessions, across three regions of Texas including Northeast Texas, Houston, and Rio Grande Valley between June 2018 and December 2022. Community Scientists stated that the feedback sessions increased their knowledge and changed their perception of research. All researchers (100%) were satisfied with the feedback and reported that it influenced their current and future research methods. Our evaluation demonstrates that the key features of the Community Scientist Program such as follow-up evaluations, effective bi-directional communication, and fair compensation transform how research is conducted and contribute to reducing health disparities.

The effectiveness of an online short-format Recovery College model: a co-learning model to support mental health.

BACKGROUND: Our societies are facing mental health challenges, which have been compounded by the Covid-19. This event led people to isolate themselves and to stop seeking the help they needed. In response to this situation, the Health and Recovery Learning Center, applying the Recovery College (RC) model, modified its training program to a shorter online format. This study examines the effectiveness of a single RC training course delivered in a shortened online format to a diverse population at risk of mental health deterioration in the context of Covid-19. METHODS: This quasi-experimental study used a one-group pretest-posttest design with repeated measures. Three hundred and fifteen (n = 315) learners agreed to take part in the study and completed questionnaires on wellbeing, anxiety, resilience, self-management, empowerment and stigmatizing attitudes and behaviors. RESULTS: Analyses of variance using a linear mixed models revealed that attending a RC training course had, over time, a statistically significant effect on wellbeing (p = 0.004), anxiety (p < 0.001), self-esteem/self-efficacy (p = 0.005), disclosure/help-seeking (p < 0.001) and a slight effect on resilience (p = 0.019) and optimism/control over the future (p = 0.01). CONCLUSIONS: This study is the first to measure participation in a single online short-format RC training course, with a diversity of learners and a large sample. These results support the hypothesis that an online short-format training course can reduce psychological distress and increase self-efficacy and help-seeking. TRIAL REGISTRATION: This study was previously approved by two certified ethics committees: Comité d'éthique de la recherche du CIUSSS EMTL, which acted as the committee responsible for the multicenter study, reference number MP-12-2021-2421, and Comité d'éthique avec les êtres humains de l'UQTR, reference number CER-20-270-07.01.

Our business, not the robot's: family conversations about privacy with social robots in the home.

The targeted use of social robots for the family demands a better understanding of multiple stakeholders' privacy concerns, including those of parents and children. Through a co-learning workshop which introduced families to the functions and hypothetical use of social robots in the home, we present preliminary evidence from 6 families that exhibits how parents and children have different comfort levels with robots collecting and sharing information across different use contexts. Conversations and booklet answers reveal that parents adopted their child's decision in scenarios where they expect children to have more agency, such as in cases of homework completion or cleaning up toys, and when children proposed what their parents found to be acceptable reasoning for their decisions. Families expressed relief when they shared the same reasoning when coming to conclusive decisions, signifying an agreement of boundary management between the robot and the family. In cases where parents and children did not agree, they rejected a binary, either-or decision and opted for a third type of response, reflecting skepticism, uncertainty and/or compromise. Our work highlights the benefits of involving parents and children in child- and family-centered research, including parental abilities to provide cognitive scaffolding and personalize hypothetical scenarios for their children.

Multi-modal co-learning with attention mechanism for head and neck tumor segmentation on 18FDG PET-CT.

PURPOSE: Effective radiation therapy requires accurate segmentation of head and neck cancer, one of the most common types of cancer. With the advancement of deep learning, people have come up with various methods that use positron emission tomography-computed tomography to get complementary information. However, these approaches are computationally expensive because of the separation of feature extraction and fusion functions and do not make use of the high sensitivity of PET. We propose a new deep learning-based approach to alleviate these challenges. METHODS: We proposed a tumor region attention module that fully exploits the high sensitivity of PET and designed a network that learns the correlation between the PET and CT features using squeeze-and-excitation normalization (SE Norm) without separating the feature extraction and fusion functions. In addition, we introduce multi-scale context fusion, which exploits contextual information from different scales. RESULTS: The HECKTOR challenge 2021 dataset was used for training and testing. The proposed model outperformed the state-of-the-art models for medical image segmentation; in particular, the dice similarity coefficient increased by 8.78% compared to U-net. CONCLUSION: The proposed network segmented the complex shape of the tumor better than the state-of-the-art medical image segmentation methods, accurately distinguishing between tumor and non-tumor regions.

Co-learning synaptic delays, weights and adaptation in spiking neural networks.

Spiking neural network (SNN) distinguish themselves from artificial neural network (ANN) because of their inherent temporal processing and spike-based computations, enabling a power-efficient implementation in neuromorphic hardware. In this study, we demonstrate that data processing with spiking neurons can be enhanced by co-learning the synaptic weights with two other biologically inspired neuronal features: (1) a set of parameters describing neuronal adaptation processes and (2) synaptic propagation delays. The former allows a spiking neuron to learn how to specifically react to incoming spikes based on its past. The trained adaptation parameters result in neuronal heterogeneity, which leads to a greater variety in available spike patterns and is also found in the brain. The latter enables to learn to explicitly correlate spike trains that are temporally distanced. Synaptic delays reflect the time an action potential requires to travel from one neuron to another. We show that each of the co-learned features separately leads to an improvement over the baseline SNN and that the combination of both leads to state-of-the-art SNN results on all speech recognition datasets investigated with a simple 2-hidden layer feed-forward network. Our SNN outperforms the benchmark ANN on the neuromorphic datasets (Spiking Heidelberg Digits and Spiking Speech Commands), even with fewer trainable parameters. On the 35-class Google Speech Commands dataset, our SNN also outperforms a GRU of similar size. Our study presents brain-inspired improvements in SNN that enable them to excel over an equivalent ANN of similar size on tasks with rich temporal dynamics.

Co-design, co-learning, and co-production of an app for pancreatic cancer patients-the "Pancreas Plus" study protocol.

Background: Pancreatic cancer is a malignant and complex tumor that often leads to an adverse prognosis. Patients need to face a challenging treatment path, which involves highly-specialized multidisciplinary professionals. The complexity of the disease requires the development of dedicated tools to support patients in their care journey. Co-production stands as a valuable strategy in oncological care to engage patients in understanding their care journey and behaving accordingly to get the best possible clinical outcome. Methods: The non-profit association Unipancreas, active in promoting the latest advances in pancreatic cancer care and in supporting pancreatic cancer patients, has partnered with a multidisciplinary group of professionals to conceive the brand new program "Pancreas Plus" to employ a co-design, co-learning, and co-production path to design an app devoted to pancreatic cancer patients to assist them during their treatment and follow-up journey. The app, which is the outcome of a multi-stakeholder engagement project, offers health information and medical advice specifically tailored on the pancreatic cancer disease. The article reports the research protocol, which may be replicated for the design of other e-health tools focusing on different conditions. Discussion: The study's output will be an app that sees the pancreatic cancer patient as the main beneficiary but which can gather and address the interests and needs of all meaningful stakeholders, including clinicians, researchers, healthcare and educational institutions, and non-profit associations. Registration: Given the type of study, no registration is required.

Human intelligence-based metaverse for co-learning of students and smart machines.

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students' learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software Engineering and learning English.

BIDL: a brain-inspired deep learning framework for spatiotemporal processing.

Brain-inspired deep spiking neural network (DSNN) which emulates the function of the biological brain provides an effective approach for event-stream spatiotemporal perception (STP), especially for dynamic vision sensor (DVS) signals. However, there is a lack of generalized learning frameworks that can handle various spatiotemporal modalities beyond event-stream, such as video clips and 3D imaging data. To provide a unified design flow for generalized spatiotemporal processing (STP) and to investigate the capability of lightweight STP processing via brain-inspired neural dynamics, this study introduces a training platform called brain-inspired deep learning (BIDL). This framework constructs deep neural networks, which leverage neural dynamics for processing temporal information and ensures high-accuracy spatial processing via artificial neural network layers. We conducted experiments involving various types of data, including video information processing, DVS information processing, 3D medical imaging classification, and natural language processing. These experiments demonstrate the efficiency of the proposed method. Moreover, as a research framework for researchers in the fields of neuroscience and machine learning, BIDL facilitates the exploration of different neural models and enables global-local co-learning. For easily fitting to neuromorphic chips and GPUs, the framework incorporates several optimizations, including iteration representation, state-aware computational graph, and built-in neural functions. This study presents a user-friendly and efficient DSNN builder for lightweight STP applications and has the potential to drive future advancements in bio-inspired research.

Testing theory of change assumptions of health behavior change interventions: A blended approach exploring local contexts.

This paper used a blended approach that involves multiple techniques to, first, test a set of assumptions around a health behavior change communication intervention theory of change (ToC) and, second, surface some unidentified assumptions involving the local context. The intervention was integrated with women's self-help groups (SHGs) in Uttar Pradesh, India. The key assumption tested in this paper was the linkage between SHG membership, program exposure, and maternal, newborn, and child health practices. Learnings were substantiated through empirical investigations, including structural equation modeling and mediation analysis, as well as 'co-learning' workshops within the community. The workshops aimed to capture and interpret the heterogeneity of local contexts through deep dialogs with the community and program implementers at various levels. Statistical analyses indicated a significant association between the amount of women's program exposure and their health practices. SHG membership was shown to affect maternal health practices; however, it did not have a direct effect on neonatal or child health practices. The 'co-learning' workshops revealed crucial aspects, such as prevailing socio-cultural norms, which prevented pregnant or recently delivered women from participating in SHG meetings. This paper encourages evaluators to work with the community to interpret and co-construct meaning in unpacking the contextual forces that seldom appear in the program ToC.

Learner Experience of an Online Co-Learning Model to Support Mental Health during the COVID-19 Pandemic: A Qualitative Study.

The COVID-19 pandemic has had a negative impact on the mental health of the population such as increased levels of anxiety, psychological distress, isolation, etc. Access to mental health services has been limited due to the "overflow" of demands. The Recovery College (RC) model, an education-based approach, has addressed this challenge and provided online well-being and mental health courses to at-risk populations. The RC model proposes a co-learning space in an adult education program where learners from diverse backgrounds collectively learn and empower themselves to better address psychological well-being and mental health issues. The aim of this study was to document the experience of learners who participated in online RC courses during the COVID-19 pandemic and the perceived impact of these courses on their mental health. A qualitative interpretative descriptive study design was employed, and Miles and Huberman's stepwise content analysis method was used to mine the data for themes. Fourteen structured online interviews were conducted with a sample representative of the diversity of learners. Five categories of themes emerged: (1) updating and validating your mental health knowledge, (2) taking care of yourself and your mental health, (3) improving and modifying your behaviors and practices, (4) changing how you look at yourself and others, and (5) interacting and connecting with others. Results suggest that online RC courses can be an effective strategy for supporting individual self-regulation and empowerment, breaking social isolation, and reducing the effects of stress in times of social confinement measures and limited access to care.

A Collaborative Approach for the Development and Application of Machine Learning Solutions for CMR-Based Cardiac Disease Classification.

The quality and acceptance of machine learning (ML) approaches in cardiovascular data interpretation depends strongly on model design and training and the interaction with the clinical experts. We hypothesize that a software infrastructure for the training and application of ML models can support the improvement of the model training and provide relevant information for understanding the classification-relevant data features. The presented solution supports an iterative training, evaluation, and exploration of machine-learning-based multimodal data interpretation methods considering cardiac MRI data. Correction, annotation, and exploration of clinical data and interpretation of results are supported through dedicated interactive visual analytics tools. We test the presented concept with two use cases from the ACDC and EMIDEC cardiac MRI image analysis challenges. In both applications, pre-trained 2D U-Nets are used for segmentation, and classifiers are trained for diagnostic tasks using radiomics features of the segmented anatomical structures. The solution was successfully used to identify outliers in automatic segmentation and image acquisition. The targeted curation and addition of expert annotations improved the performance of the machine learning models. Clinical experts were supported in understanding specific anatomical and functional characteristics of the assigned disease classes.

Plastics in our ocean as transdisciplinary challenge.

This conference report summarizes the current challenges of researching microplastics pollution in the ocean as debated by international experts and stakeholders at a workshop held in San Sebastián, Spain, 1-2 October 2019. The transdisciplinary, co-learning approach of this report stressed the need to incorporate multiple perspective in solving the problem of microplastics and resulted in three proposed actions: (i) filtering microplastics from waste waters; (ii) mandatory ecolabels on plastic products packages; and (iii) circular economy of packaging plastics.

Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning.

Becoming a well-functioning team requires continuous collaborative learning by all team members. This is called co-learning, conceptualized in this paper as comprising two alternating iterative stages: partners adapting their behavior to the task and to each other (co-adaptation), and partners sustaining successful behavior through communication. This paper focuses on the first stage in human-robot teams, aiming at a method for the identification of recurring behaviors that indicate co-learning. Studying this requires a task context that allows for behavioral adaptation to emerge from the interactions between human and robot. We address the requirements for conducting research into co-adaptation by a human-robot team, and designed a simplified computer simulation of an urban search and rescue task accordingly. A human participant and a virtual robot were instructed to discover how to collaboratively free victims from the rubbles of an earthquake. The virtual robot was designed to be able to real-time learn which actions best contributed to good team performance. The interactions between human participants and robots were recorded. The observations revealed patterns of interaction used by human and robot in order to adapt their behavior to the task and to one another. Results therefore show that our task environment enables us to study co-learning, and suggest that more participant adaptation improved robot learning and thus team level learning. The identified interaction patterns can emerge in similar task contexts, forming a first description and analysis method for co-learning. Moreover, the identification of interaction patterns support awareness among team members, providing the foundation for human-robot communication about the co-adaptation (i.e., the second stage of co-learning). Future research will focus on these human-robot communication processes for co-learning.

"Health in" and "Health of" Social-Ecological Systems: A Practical Framework for the Management of Healthy and Resilient Agricultural and Natural Ecosystems.

The past two decades have seen an accumulation of theoretical and empirical evidence for the interlinkages between human health and well-being, biodiversity and ecosystem services, and agriculture. The COVID-19 pandemic has highlighted the devastating impacts that an emerging pathogen, of animal origin, can have on human societies and economies. A number of scholars have called for the wider adoption of "One Health integrated approaches" to better prevent, and respond to, the threats of emerging zoonotic diseases. However, there are theoretical and practical challenges that have precluded the full development and practical implementation of this approach. Whilst integrated approaches to health are increasingly adopting a social-ecological system framework (SES), the lack of clarity in framing the key concept of resilience in health contexts remains a major barrier to its implementation by scientists and practitioners. We propose an operational framework, based on a transdisciplinary definition of Socio-Ecological System Health (SESH) that explicitly links health and ecosystem management with the resilience of SES, and the adaptive capacity of the actors and agents within SES, to prevent and cope with emerging health and environmental risks. We focus on agricultural transitions that play a critical role in disease emergence and biodiversity conservation, to illustrate the proposed participatory framework to frame and co-design SESH interventions. Finally, we highlight critical changes that are needed from researchers, policy makers and donors, in order to engage communities and other stakeholders involved in the management of their own health and that of the underpinning ecosystems.

"We are the stakeholders with the most at stake": scientific and autism community co-researchers reflect on their collaborative experience in the CONNECT project.

BACKGROUND: Little research describes the everyday challenges and needs of autistic adults. In order to fill this data gap, the CONtiNuity of carE and support for autistiC adulTs (CONNECT) project set out to learn about the health and well-being of autistic adults as well as their service and support needs. To do so, CONNECT welcomed autistic adults and caregivers of autistic adults as members of the research team, alongside researchers, policy-makers, service providers and health professionals. Autistic adults were involved in every stage of the research project and participated in team meetings held several times a year as well as in numerous email exchanges. METHODS: Two feedback questionnaires were designed for this study: one for the scientific co-researchers and one for the autism community co-researchers (the project's "patient partners"). Although the surveys varied from one another, they probed respondents to provide critical and constructive comments on issues that were central to their engagement in CONNECT. Four scientific co-researchers and four autism community co-researchers filled out the questionnaires. A comparative analysis was carried out on the responses provided to the open- and closed-ended survey questions as well as on complimentary data collected from the team's documents. RESULTS: CONNECT was seen as a positive experience for both groups. Highlights included: helping tailor and design research and its relevant materials to better suit the needs of the autistic community; establishing relationships and creating long-lasting friendships with other autistic adults; gaining a better understanding of the research process; and forging new connections with regional, national and international stakeholders. Areas for improvement include: establishing clear roles, responsibilities and expectations from the start; outlining a strategy to address unforeseen changes in project leadership; and creating a platform allowing for the involvement and participation of a more representative sample of adults on the autism spectrum. CONCLUSIONS: While not without its challenges, CONNECT demonstrates that a collaborative multi-stakeholder approach engaging autistic adults can be an effective model for conducting research on adult autism. Autistic adults and their caregivers can make the research process more open and accessible and make its outputs more relevant, useful and meaningful to the wider autistic adult community.

Community of practice: an effective mechanism to strengthen capacity in climate change and health.

SETTING: Climate change is one of the greatest threats to global health in the twenty-first century and has recently been declared a health emergency. The lack of effective dissemination of emerging evidence on climate change health risks, effects, and innovative interventions to health professionals presents one of the greatest challenges to climate action today. INTERVENTION: To identify and address the knowledge gaps at the intersection of health and climate change, the Canadian Coalition for Global Health Research (CCGHR) established a Working Group on Climate Change and Health (WGCCH). WGCCH is evolving organically into a community of practice (CoP) that aims to elevate knowledge brokering on climate change and health and expand to global multi-, inter-, and transdisciplinary realms. OUTCOMES: To date, the WGCCH established a regular webinar series to share expert knowledge from around the world on intersections between climate change and health, developed short summaries on climate change impacts on broad health challenges, supported young professional training, and enhanced climate health research capacity and skills through collegial network development and other collaborative projects that emerged from CoP activities. IMPLICATIONS: This paper proposes that WGCCH may serve as an example of an effective strategy to address the lack of opportunities for collaborative engagement and mutual learning between health researchers and practitioners, other disciplines, and the general public. Our experiences and lessons learned provide opportunities to learn from the growing pains and successes of an emerging climate change and health-focused CoP.

RéSUMé: LIEU: Le changement climatique est l'une des plus grandes menaces pour la santé mondiale au 21e siècle et a récemment été déclaré une urgence sanitaire. Le manque de diffusion efficace des données obtenues concernant les risques pour la santé liés au changement climatique, les impacts et les interventions innovantes auprès des professionnels de la santé constitue aujourd'hui l'un des plus grands défis de l'action climatique. INTERVENTION: Pour identifier et combler les lacunes de connaissances communes à la santé et aux changements climatiques, la Coalition canadienne pour la recherche en santé mondiale (CCRSM) a créé un groupe de travail sur les changements climatiques et la santé (WGCCH). WGCCH évolue organiquement vers une communauté de pratiques (CoP) qui vise à élever le niveau de développement de connaissances liant les changements climatiques à la santé et à s'étendre aux domaines mondiaux multi, inter et transdisciplinaires. RéSULTATS: À ce jour, le WGCCH a lancé une série de webinaires réguliers pour diffuser les connaissances d'experts du monde entier sur les liens entre les changements climatiques et la santé, a élaboré de courts exposés sur les impacts des changements climatiques sur les grands défis de santé, a soutenu la formation de jeunes professionnels et a amélioré la capacité et les compétences en matière de recherche en santé climatique à travers le développement d'un réseau universitaire et d'autres projets de collaboration dont ont émergé des activités de la CoP. IMPLICATIONS: Cet article propose que le WGCCH puisse servir d'exemple d'une stratégie efficace pour remédier au manque d'opportunités d'engagement collaboratif et d'apprentissage mutuel entre les chercheurs et les praticiens de la santé, d'autres disciplines et le grand public. Nos expériences et leçons apprises offrent des occasions de tirer des leçons des peines et des succès croissants d'une CoP axée sur le changement climatique et la santé.

Engaging Stakeholders and Patient Partners.

Recent advances in engagement of stakeholders and patient partners in clinical research have bridged the disconnect between researchers and stakeholders, resulting in improved research goals with relevant outcomes, increased clinical trial enrollment, and improved communication of research results. This article focuses on the mechanisms, challenges, and benefits of patient and stakeholder engagement, with strategies for improvement. The 3 stages of clinical research and key iterative steps to create a reciprocal relationship are presented. Despite recent advances in stakeholder engagement, additional investigation and improved reporting of methods will facilitate strong reciprocal relationships between researchers and stakeholders.