Set Framework Research Articles

Management of uncontrolled/recurrent epistaxis by ligation or cauterization of the sphenopalatine artery: a scoping review.

The control of epistaxis has always posed a significant challenge for otolaryngologists. One of the most viable options to address refractory cases is the ligation or cauterization of the sphenopalatine artery. The objective of this study was to assess the efficacy, safety, and long-term outcomes of these interventions. Two independent otolaryngologists conducted a comprehensive search for studies dealing with management of uncontrolled/recurrent epistaxis by consulting the main scientific databases on the web, including PubMed, Google Scholar, Medline, EMBASE, Web of Science, and the Cochrane Library. The systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. The criteria for considering studies for the review were based on the population, intervention, comparison, outcome, timing and setting (PICOTS) framework. Sixteen studies were included in the systematic review, comprising a total of 454 patients. Among these, 289 individuals underwent ligation of the sphenopalatine artery, while 100 underwent cauterization of the same artery. Additionally, 56 patients underwent both ligation and cauterization of the sphenopalatine artery during the same surgery. The incidence of rebleeding and complications was respectively 12.1% (55/454) and 3% (14/454), resulting in relatively low rates in both cases. Our review emphasizes the increasing importance of surgical approaches, specifically ligation or cauterization of the sphenopalatine artery, in addressing refractory cases. The low incidence of complications, predominantly temporary decreased lacrimation in patients undergoing ligation of the sphenopalatine artery, highlights the safety and feasibility of these interventions.

Was priority setting considered in COVID-19 response planning? A global comparative analysis

BackgroundThe COVID-19 pandemic forced governments across the world to consider how to prioritize resource allocation. Most countries produced pandemic preparedness plans that guide and coordinate healthcare, including how to allocate scarce resources such as ventilators, human resources, and therapeutics. The objective of this study was to compare and contrast the extent to which established parameters for effective priority setting (PS) were incorporated into COVID-19 pandemic response planning in several countries around the world. MethodsWe used the Kapriri and Martin framework for effective priority setting and performed a quantitative descriptive analysis to explore whether and how countries’ type of health system, political, and economic contexts impacted the inclusion of those parameters in their COVID-19 pandemic plans. We analyzed 86 country plans across six regions of the World Health Organization. ResultsThe countries sampled represent 40% of nations in AFRO, 54.5% of EMRO, 45% of EURO, 46% of PAHO, 64% of SEARO, and 41% of WPRO. They also represent 39% of all HICs in the world, 39% of Upper-Middle, 54% of Lower-Middle, and 48% of LICs. No pattern in attention to parameters of PS emerged by WHO region or country income levels. The parameters: evidence of political will, stakeholder participation, and use of scientific evidence/ adoption of WHO recommendations were each found in over 80% of plans. We identified a description of a specific PS process in 7% of the plans; explicit criteria for PS in 36.5%; inclusion of publicity strategies in 65%; mention of mechanisms for appealing decisions or implementing procedures to improve internal accountability and reduce corruption in 20%; explicit reference to public values in 15%; and a description of means for enhancing compliance with the decisions in 5%. ConclusionThe findings provide a basis for policymakers to reflect on their prioritization plans and identify areas that need to be strengthened. Overall, there is little consideration for explicit prioritization processes and tools and restricted attention to equity considerations; this may be a starting point for policymakers interested in improving future preparedness and response planning. Although the study focused on the COVID-19 pandemic, priority setting remains one of the policymakers’ most prominent challenges. Policymakers should consider integrating systematic priority setting in their routine decision-making processes.

The rhythmic mind: brain functions of percussionists in improvisation.

Percussionists stand out for their expertise in rhythm, with the network for musical rhythm (NMR) serving a vital neurological function in their improvisation, which is deeply rooted in comprehensive musical knowledge. Our research examines the central representations of various improvisation tactics used by percussionists and investigates the interactions between the NMR and other relevant neural networks. Twenty-five percussionists participated in functional magnetic resonance imaging (fMRI) sessions, which included two cognitive strategies of improvisation. Structural improvisation (SIMP) emphasized rhythmic patterns, while free improvisation (FIMP) focused on musical spontaneity. Sight-reading scenario served as the reference condition. Paired t-tests were utilized for comparative analyses. The findings revealed a dynamic interplay characterized by increased activity in the executive control network and NMR, along with decreased activity in the default mode network during SIMP. During FIMP, heightened activity was observed in the executive control network, NMR, limbic, and memory systems. In both SIMP vs. sight-reading and FIMP vs. sight-reading comparisons, the visual network's activity decreased, a trend also observed in the comparative analysis of FIMP vs. SIMP. In SIMP, percussionists leverage external rhythmic signals, resulting in heightened NMR and ECN activity and reduced DMN activity. In contrast, FIMP is characterized by a rise in activity within the NMR, ECN, limbic system, memory system, and reward system, underscoring the vital roles of motivation and memory in the rapid production of spontaneous musical ideas within set frameworks. The diminished activity in the visual network during FIMP compared to SIMP suggests less reliance on visual stimuli in FIMP. These findings suggest that various improvisational tactics may engage different neural pathways.

A comparative study of critical thinking implementation in face-to-face and online classrooms: Medical students’ critical thinking and reading comprehension

Background: The enhancement of critical thinking (CT) is an essential goal in the educational strategic framework of medical universities across the globe. Therefore, it is of utmost importance to evaluate the competence in CT abilities as well as the inclination of medical students to actively participate in CT practices. Moreover, educational decision-makers believe that granting medical students the opportunity to utilize technology can enhance their academic performance and stimulate their CT ability level. Hence, this research aimed to compare the influence of implementing a CT framework in traditional classroom settings and online platforms on the CT and reading comprehension of medical students. Methods: Over four months, two groups of 60 medical students (30 students in each group) were exposed to two environments of face-to-face and online English language instruction. The current quasi-experimental study was conducted at a general English language classroom, situated in Islamic Azad University, Kerman branch. Pre-tests and post-tests of CT and reading comprehension were used to measure learners’ improvements in CT and reading comprehension levels before and after the project. Results: The results of this study showed some improvements in the CT and reading comprehension abilities of both face-to-face and online groups (P<0.01). However, the improvements were particularly noticeable in the online group regarding the reading comprehension (M=17.98) and CT (M=107.462) variables compared to the face-to-face group (M=11.39) and (M=51.438). Conclusion: Medical education experts strongly believe that developing and nurturing CT skills among medical students is crucial for improving the overall quality of medical education. In today’s globalized era, the importance of CT cannot be overstated, as it equips students with essential skills needed to navigate complex challenges. Integrating CT into online learning environments is a testament to the progressive nature of medical education in the modern world.

Hyperparameter optimization of two-branch neural networks in multi-target prediction

As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations like Auto-WEKA and Auto-sklearn typically focus on classical machine learning (ML) tasks such as classification and regression. Our work can be seen as the first attempt at offering a single AutoML framework for most problem settings that fall under the umbrella of multi-target prediction, which includes popular ML settings such as multi-label classification, multivariate regression, multi-task learning, dyadic prediction, matrix completion, and zero-shot learning. Automated problem selection and model configuration are achieved by extending DeepMTP, a general deep learning framework for MTP problem settings, with popular hyperparameter optimization (HPO) methods. Our extensive benchmarking across different datasets and MTP problem settings identifies cases where specific HPO methods outperform others.

A Systematic Approach for Evaluating and Selecting Healthcare Waste Treatment Devices using OWCM-CODAS and Triangular Neutrosophic Sets

Healthcare is a fundamental aspect of human life, impacting individuals, communities, and societies. Investing in healthcare infrastructure, services, and education is essential for fostering a healthy, thriving population. Healthcare waste management is a critical aspect of public health, and it requires a concerted effort from healthcare facilities, governments, and communities to ensure that waste is managed in a way that minimizes risks to human health and the environment. This paper proposes a hybrid methodology combining the Opinion Weight Criteria Method (OWCM) and the Combinative Distance-Based Assessment (CODAS) within the framework of Triangular Neutrosophic Sets (TNS) to evaluate and select the optimal healthcare waste treatment devices. The proposed methodology balances various conflicting criteria and incorporates uncertainty in expert assessments to ensure a robust decision-making process. The paper also provides a comprehensive review of healthcare waste management devices and highlights the need for a comprehensive approach to healthcare waste management. The proposed methodology is applied to a case study involving evaluating healthcare waste treatment devices based on different factors such as economic, environmental, technological, and social aspects. The results show that the OWCM-CODAS methodology, integrated with TNS, effectively evaluates and ranks healthcare waste treatment devices based on multiple criteria. The sensitivity analysis demonstrates the robustness of the proposed model, and the results indicate that the model's performance and the relative ranking of the alternatives are not sensitive to changes in the threshold parameter τ within the tested range.

Fairness in accessibility of public service facilities

Our paper studies a fair stochastic facility location problem with congestion under the max-min principle. We analyze the price of fairness in a two-location problem and develop a tractable optimization framework for the general multi-location setting. Evaluating the fair solution against the utilitarian solution on Buffalo's demographic data reveals that implementing a fair solution can substantially improve fairness measures (up to 98%) with relatively limited impact on the overall service quality.

Educational Big Data Analytics for Futuristic Smart Learning Using Deep Learning Techniques

The goal is to use the massive amounts of data created by digital education systems to develop intelligent and adaptable learning environments that are particularly suited to each student’s requirements. The rapid digitization of education systems has led to the proliferation of educational big data, presenting unprecedented opportunities to reshape learning environments into intelligent, responsive spaces that adapt to the needs of individual learners. This paper explores the integration of advanced deep learning techniques with educational big data analytics to forge the path towards futuristic smart learning ecosystems. By leveraging robust datasets derived from a myriad of educational interactions, ranging from student performance metrics to engagement patterns in digital learning platforms, we propose a multi-tiered analytical framework that harnesses the predictive power of deep learning. We commence by elucidating the scope and scale of educational big data, highlighting its potential to provide granular insights into student learning processes. The paper then delineates the architecture of a deep learning-based analytical model designed to process complex, multidimensional educational datasets. This model applies state-of-the-art algorithms to perform tasks such as predictive analytics for student performance, personalized content recommendation, and real-time engagement monitoring. Central to our discussion is the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs) in deciphering patterns and trends that escape traditional analytical methodologies. We emphasize the capacity of these techniques to capture the subtleties of learner behavior and to facilitate the development of adaptive learning pathways. Furthermore, we address the challenges of integrating deep learning with educational big data, including issues of data privacy, computational demands, and the need for robust model interpretation. The paper presents a series of case studies that demonstrate the successful application of our proposed framework in various educational settings, from K-12 to higher education and continuous professional development.

The influence of ESG score on financial performance: Evidence from the European health care industry

AbstractCompany managers are increasingly recognizing that sustainable practices are not just ethical imperatives but potential drivers of long‐term value. To determine the degree to which this is grounded in business reality, a growing body of empirical studies have examined whether higher environmental, social, and governance (ESG) performance predicts better financial performance. However, conflicting findings have emerged, with reviews highlighting the role that methodological choices, normative setting, and industry can play in moderating that relationship. Unlike other industries, health care has been the focus of only a handful of heterogeneous quantitative studies. To contribute to this evidence base for the European context, the present study comprehensively examined whether and how ESG score can influence both accounting‐ and market‐based measures of financial performance. Company data from the top health care companies listed in the STOXX 600 Index over the last decade (2012–2022) were gathered and analyzed. The analysis results confirmed that a marked heterogeneity of effects across the different ESG scores and measures of financial performance exists, which is not merely due to differences in empirical strategy, with modest positive effects being estimated in most cases. Of the three pillars, the governance score consistently predicted more positive financial performance among the sample of listed European companies. The environmental pillar score only influenced accounting‐based measures, though inconsistently, whereas the social pillar did not seem to have any predicting role for accounting‐based measures. Future research should consider replicating the proposed analytical framework in other data settings and normative contexts to strengthen the current evidence base and therefore deepen our understanding of the mechanisms underlying the investigated relationships in the health care industry.

On the Growing Universe of Causal Set Theory—An Order-Type Approach

We investigate a model of becoming—classical sequential growth (CSG)—that has been proposed within the framework of causal sets (causets), with the latter defined as order types of certain partial orderings. To investigate how causets grow, we introduce special sequences of causets, which we call “csg-paths”. We prove a number of results concerning relations between csg-paths and causets. These results paint a highly non-trivial picture of csg-paths. There are uncountably many csg-paths, all of them sharing the same beginning, after which they branch. Every infinite csg-path achieves in the limit an infinite causet, and vice versa, every infinite causet is achieved in the limit by an infinite csg-path. However, coalescing csg-paths, i.e., ones that achieve the same causet even after forking off at some point, are ubiquitous.

Clinically relevant estimation of minimal number of trials for the uncontrolled manifold analysis

Movement biomarkers are crucial for assessing sensorimotor impairments and tracking the effects of interventions over time. The Uncontrolled Manifold (UCM) analysis has been proposed as a novel biomarker for evaluating movement stability and coordination in various motor tasks across neurological and musculoskeletal disorders. Through inter-trial analysis, the UCM partitions the variance of elemental variables (e.g., finger forces) into components that affect (VORT) and do not affect (VUCM) a performance variable (e.g., total force). A third index, ΔV, is computed as the normalized difference between VORT and VUCM. However, the minimum number of trials required to achieve stable UCM estimates, considering its clinimetric properties, is unknown. This study aimed to determine the minimal number (N) of trials for UCM estimates by computing bootstrap estimates of standard errors (SE) at different N trials using thresholds based on the minimal detectable change (MDC, i.e., the minimum change in an outcome measure beyond measurement error). Thirteen adults (24.6 ± 1.1 years old) performed a finger-pressing coordination task. We computed the 95 % confidence intervals (CI) of bootstrap SE distributions for each UCM estimate and detected the lowest number of trials with the 95 % CI of SE below each MDC threshold. We found the minimal N of trials required was VUCM = 14, VORT = 4 and ΔV = 18. Our findings highlight that a relatively low number of trials (i.e., N = 18) are sufficient to compute all UCM estimates beyond the MDC, supporting the use of the UCM framework in clinical settings where many repetitions of a motor task are not practical.

Adherence to weekly iron folic acid supplementation and associated factors among adolescent girls – A mixed-method study

ABSTRACT Context: Despite the Weekly Iron folic acid supplementation (WIFS) program, the prevalence of anaemia among adolescent girls remains high. Phase 1 Indian Council of Medical Research (ICMR) task force study conducted in 2016 in Kallur showed that the IFA provision rate for adolescent girls was 72% but the consumption rate was only 15% in the Kallur area. The present study was done to identify the gaps for the difference between provision and consumption rate of weekly IFA tablets among adolescent girls using the WHO conceptual framework in home-based settings. Materials and Methods: This crosssectional study with a mixedmethod design was conducted from October 2020 to December 2021. Quantitative data were collected from 972 adolescent girls and their parents using a structured pretested questionnaire, whereas qualitative exploration was done by focus group discussions. Descriptive analysis and bivariate analysis were used to analyse the quantitative data. Qualitative data were analysed and integrated with quantitative results. Results: The overall number of girls who were aware of Iron Folic acid therapy (IFAT) was 704 (72%). However, only 132 (13%) adolescent girls were found to be adherent to IFA therapy. Multivariable regression analysis revealed that side effects encountered on intake of IFAT (Odds ratio (OR) =0.5, P = 0.009) were associated with higher rates of nonadherence, whereas regular supply (OR = 13.6, P = 0.000), health education to parents (OR = 2.76, P = 0.002), and experiencing benefits (OR = 1.72, P = 0.006) were associated with higher rates of adherence. These were substantiated by qualitative findings. Conclusions: Ignorance on the impact of anaemia on adolescent health, fear of side effects, unpleasant effects experienced on intake of Iron folic acid (IFA), and inadequate counselling determines the adherence to weekly IFA supplements among adolescent girls.

Impact of the SECI Entrepreneurial Development Model on Entrepreneurial Activities: Unleashing Entrepreneurial Knowledge Creation Potential

This study uses a novel idea of the entrepreneurial development process using the SECI framework that addresses how implicit and explicit entrepreneurial knowledge is transformed into new startups. There are four distinct modes: socialization, externalization, combination, and internalization. This study analyzes the influence of the SECI entrepreneurial development model on entrepreneurial activities across the globe, utilizing the data set from the Global Entrepreneurship Monitor (GEM). The GEM data set is an extensive and dependable resource that provides information on all facets of entrepreneurial development, including entrepreneurial activity, attitudes, intentions, and contextual factors, including government policies and social environment. The study utilizes panel data and examines the underlying hypotheses. The findings reveal the underlying process of entrepreneurial endeavors through the SECI framework. This study enhances the existing body of knowledge on knowledge management and entrepreneurship by presenting empirical evidence that supports the validity and practicality of the SECI framework in various settings. The study has practical implications for policymakers, scholars, and practitioners aiming to cultivate a culture of innovation and entrepreneurship.

Techno-economic and environmental analysis of organic municipal solid waste for energy production

Addressing the critical conundrum of escalating municipal solid waste (MSW) and shrinking landfill spaces in urban areas, this research pioneers a sustainable approach for Bangladesh by exploring the potential of biogas production from MSW. Distinctly, it fills the research gap by providing a detailed techno-economic and environmental analysis of decentralized fixed-dome anaerobic digestion facilities in the urban context of Chittagong, Bangladesh, a domain previously underexplored. Our findings demonstrate the feasibility of converting MSW into a renewable energy source, offering an innovative solution that simultaneously tackles waste management and energy generation challenges. Each proposed plant showcases the capability to generate 536 m³ of biogas daily, sufficient to power a 50 kW gas engine and supply 44 households, thereby contributing significantly to urban waste reduction and CO2 emissions mitigation by approximately 500 tons monthly. The economic analysis reveals an attractive investment payback period of two years, underscoring the model's viability and its potential as a replicable framework for similar urban settings grappling with waste management crises. This study not only bridges a critical knowledge gap but also introduces a novel, sustainable waste-to-energy model, marking a pivotal step towards achieving energy security and environmental sustainability in developing nations.

Child-led goal setting and evaluation tools for children with a disability: A scoping review.

To examine child-led goal setting and evaluation tools and approaches for children with a disability or developmental delay. Six databases were searched for studies that included population (children aged less than 18 years with disability or developmental delay); construct (child-led goal setting tool or approach); and context (developmental therapy or rehabilitation). The utility of tools and approaches across the goal setting and evaluation process was investigated using abductive content analysis. Fifty articles met the inclusion criteria. Three approaches and four tools for child-led goal setting and evaluation were identified. No studies reported the clinimetric properties of tools specifically for child self-respondents. Qualitative analysis revealed six distinct goal phases in which tools and approaches were used, which were synthesized into a new framework for child-led goal setting and evaluation titled DECIDE: Direct children to goal setting; Elicit goal topics and priorities; Construct a goal statement; Indicate baseline goal performance; Develop an action plan to address the goal; and Evaluate goal progress after the intervention. Children actively participated in goal setting and evaluation across six DECIDE goal phases. Further clinimetric information is required to support use of goal setting and evaluation tools with child self-respondents. Future research should emphasize the development of multi-phase goal setting tools and approaches for diverse populations of children.

Assessment of wave energy location, technology, and converter toward sustainability using integrated spherical fuzzy MCDM approach

Due to climate change, fossil fuel reduction, growing energy usage, and ecological impacts, the need for renewable energy sources is rising quickly. A large amount of energy comes from the ocean through the tide; renewable energy must be exploited. One important energy source is wave energy converters (WECs), which enhance efficiency and grow energy. This study presented a case study from three layers of the Red Sea in Egypt for selecting optimal wave energy location (WEL), wave energy technology (WET), and WEC. The spherical fuzzy set (SFS) framework deals with uncertainty and vague information in this selection process. The SFS is integrated with multi-criteria decision-making (MCDM) to deal with various criteria in each layer. This study uses two MCDM methods under the SFS environment: The Stepwise Weighted Assessment Ratio Analysis (SWARA) method and the Weighted Euclidean Distance Based Approach (WEDBA) method. The SF-SWARA method is used to compute the criteria weights in each layer. Then, the SF-WEDBA method ranks the alternatives in each layer. The main results in each layer show that the accessibility criterion, Economic factors, and energy production criterion have the highest weight in the three layers. Sensitivity analysis and comparative analysis are conducted in each layer.

Kernel-based Sensitivity Analysis for (Excursion) Sets

In this article, we aim to perform sensitivity analysis of set-valued models and, in particular, to quantify the impact of uncertain inputs on feasible sets, which are key elements in solving a robust optimization problem under constraints. While most sensitivity analysis methods deal with scalar outputs, this article introduces a novel approach to perform sensitivity analysis with set-valued outputs. Our innovative methodology is designed for excursion sets, but is versatile enough to be applied to set-valued simulators, including those found in viability fields, or when working with maps like pollutant concentration maps or flood zone maps. We propose to use the Hilbert-Schmidt Independence Criterion (HSIC) with a kernel designed for set-valued outputs. After setting a probabilistic framework for random sets, a first contribution is the proof that this kernel is characteristic, an essential property in a kernel-based sensitivity analysis context. To measure the contribution of each input, we then propose to use HSIC-ANOVA indices. With these indices, we can identify which inputs should be neglected (screening) and we can rank the others according to their influence (ranking). The estimation of these indices is also adapted to the set-valued outputs. Finally, we test the proposed method on three test cases of excursion sets.

Partnering with consumers and practising clinicians to establish research priorities for public hospital maternity services.

Objective An innovative approach by two Queensland health services was taken to establish a shared maternity services' research agenda by partnering with consumers and clinicians. The objective was to set the top five research priorities to ensure that the future direction of maternity research was relevant to end-user and organisational needs. Methods A modified James Lind Alliance (JLA) methodology was applied between August 2022 and February 2023 across two south-east Queensland Health Services which included five participating maternity units and involved partnership with consumers, healthcare professionals and clinician researchers. The reporting guideline for priority setting of health research (REPRISE) was followed. Results There were 192 respondents to the initial harvesting survey, generating 461 research suggestions. These were aggregated into 122 unique questions and further summarised into a list of 44 research questions. The 157 eligible interim prioritisation survey respondents short-listed 27 questions ready for ranking at a final consensus workshop. The top five question themes were: (1) maternity care experience, engagement and outcomes of priority populations; (2) increasing spontaneous vaginal birth; (3) experiences and perceptions of woman/person-centred care; (4) best practice care during the 'fourth' trimester; and (5) antibiotic use during labour and birth. Conclusion Applying an adapted JLA framework can successfully shape and establish a research agenda within Australian health services, through partnership with consumers and practicing clinicians. This is a transparent process that strengthens the legitimacy and credibility of research agendas, and it can form a replicable framework for other settings.

Assessing the feasibility of using publicly available data sources to identify healthcare data discrepancies and enhance service delivery: a retrospective cross-sectional study of four low human index scoring countries

IntroductionQuality of care (QoC) remains a persistent challenge in countries with low Human Development Index scores (LHDIS) despite global efforts to promote universal health coverage. Addressing root causes and systematically...

Ex-ante priority setting in crop breeding: Towards inclusive innovation for impact

CONTEXTCrop breeding teams should ideally work a-priori to rank research priorities and varietal product concepts to make decisions considering social, environmental, production and market conditions that an improved crop variety will be entering upon release. Frequently, this research prioritization is guided by a cost-benefit optimization framing, which leaves the potential derived by alternative approaches, such as inclusive innovation, unexplored. OBJECTIVEIn this study, we apply PEEP (Participatory Ex-ante framework for Plant breeding), a new methodological framework for ex-ante participatory priority setting for plant breeding programs. This framework explores how, given a set of hypothetical crop varieties (varietal product concepts) it is possible to assess which will have the highest potential to deliver research impacts. PEEP is designed to support crop breeders to understand the impact potential of a new varietal product concept, and strategically make decisions based on this. METHODSPEEP ranks varietal product concepts across five impact domains: breeding, processing, marketing, climate, and cross-cutting themes. This paper reports on how an interdisciplinary breeding team from the Institute of Environment and Agricultural Research (INERA) in Burkina Faso working on cowpea (Vigna unguiculata [L.] Walp) applied PEEP and discusses the results from this application. We chose cowpea as a crop focus, based on prior research that showed that gender and social differences shape roles, responsibilities, and benefits from its cultivation in Burkina Faso. We engaged 650 stakeholders using tools and methods (choice experiment, surveys and focus group discussions) tailored to collect information according to their spheres of knowledge. RESULTS AND CONCLUSIONSWe present a reflection on the application process, results, and feedback from the INERA cowpea breeding team on these. We show that breeders and farmers ranked the potential impact of the varietal product concepts differently. We also show that differences in evaluation of impact from varietal product concepts vary based on market orientation, agroecological zones, farming systems and gender. SIGNIFICANCEWe demonstrate that PEEP contributes to inclusive agricultural innovation as a feasible method to consider the knowledge and preferences of diverse stakeholders. We show that it is informative to include diverse groups when conceptualizing new varieties before investing in varietal development. Making such research prioritization decisions early on enables breeding programs to avoid “missing the mark” on assumed impacts on smallholder women and men farmers and increases their visibility and voice in these upstream decisions.