bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deciphering stakeholder voice on the challenges of transformative healthcare 5.0 ecosystem: a quality function deployment analyses
Authors: Ojha Ravindra , Agarwal Alpana 
Published: January 2025

PurposeThe healthcare ecosystem continues to evolve with new technological developments with the support of its stakeholders. The technology-driven and patient-centric Healthcare 5.0 (H5.0) ecosystem is undergoing a transformation promising enormous benefits. However, the need to identify and understand the inherent challenges and barriers faced in the journey of H5.0 implementation and the relevant countermeasures for accelerated implementation has become critical.Design/methodology/approachThe current research paper has utilised the Delphi approach for the collection of information and applied a well-proven quality function deployment (QFD) methodology for analysis.FindingsThe house of quality (HOQ) tool from the QFD has highlighted the critical H5.0 challenges which contribute to, approximately, 60% of the total weight. The identified top five process descriptors from the developed HOQ also contribute, approximately 60% among overall countermeasures. A useful H5.0 implementation progress (HIP) index has been recommended for tracking the progress made in the H5.0 implementation journey.Originality/valueThis research is among the first that has provided the application of the HOQ approach in the QFD methodology in the domain of H5.0. It has provided useful insights to the stakeholders. Furthermore, the development of a simple and practical HIP index is another useful value addition.
Why is learning from patient safety incidents (still) so hard? A sociocultural perspective on learning from incidents in healthcare organizations
Authors: Rowland Paula , Lan Melissa F. , Wan Cecilia , Pozzobon Laura 
Published: January 2025

PurposeDespite robust quality improvement efforts in healthcare, learning from patient safety incidents remains difficult. Our study explores counter-vailing powers shaping learning processes and possibilities in healthcare organizations, with a focus on social, political and organizational dynamics of learning.Design/methodology/approachDeploying concepts of situated curriculum, boundary work and interconnected knowledge practices, we interviewed staff and physicians (n = 15) in a large Academic Health Science Centre in Canada about their experiences of incident investigations and resultant information sharing. Our analytical strategy was abductive, drawing connections to sociology of the professions and learning sciences literature.FindingsIncident investigation and follow-up processes are relatively robust for learning about incidents in the organization. However, learning from incidents remains difficult, complicated by the professional politics of incident classification, counter-vailing policies related to privacy, the organization of improvement work towards reporting, and an organizational focus on incidents with severe outcomes.Practical implicationsParticipants advocated for a broader view of incidents, moving beyond classification and investigation based on severity of outcome to also include incidents that are “learning rich”. To that end, we argue for more research on the role of Patient Safety Specialists in organizational learning and more collaborations with learning sciences.Originality/valueThis paper illuminates ways in which robust information dissemination structures are an important but insufficient condition for learning from incidents. The argument goes beyond a prescriptive approach to learning from incidents to instead explore the competing visions and values implicated with improvement practices.
Principal’s pro-social drive: bridging social responsibility and entrepreneurship in school-community leadership dynamics
Authors: Kubilay Sevda , Durnali Mehmet 
Published: January 2025

PurposeWithin the professional paradigm of education, school principals have an important role and responsibility in shaping the organizational climate, culture and practices in schools as well as occupying an important place in the school-community discourse. To this end, this research aimed to reveal the mediating role of pro-social motivations in the relationship between school principals’ social responsibility and involvement in social entrepreneurship.Design/methodology/approachA descriptive-correlational structural equation model was deployed to test a novel theory bearing upon existing research. To this end, data from 424 Turkish school principals, collected via a survey, were analyzed using descriptive, correlational and predictive algorithms in SPSS and AMOS.FindingsThe findings revealed a strong positive relationship between social responsibility and pro-social motivation and an even stronger link between social responsibility and social entrepreneurship. Additionally, pro-social motivation significantly mediated the relationship between the constructs of interest.Practical implicationsThese results hold valuable implications for designing effective schools–community relations and school principal training aligned with 21st century educational professional trends.Originality/valueAn original empirical study that combines all these said concepts and perspectives has not been conducted with school principals before. This research contributes to the tremendous potential of school principals to shape a brighter future by finding proactive solutions to persistent inequalities in society through educational responsibility and entrepreneurial leadership in the Turkish context.
Adaptive learning engine for driving marketing channel performance: a machine learning approach
Authors: Dairo Adeolu Olukorede , Szűcs Krisztián 
Published: January 2025

PurposeThis paper aims to develop and implement a machine learning recommendation engine – an adaptive learning engine that drives business revenue through the ranking and recommendation of offers at a granular customer level across the inbound marketing channels.Design/methodology/approachA data set of over 300,000 unique sample of mobile customers was extracted and prepared. The gradient boosting machine (GBM) algorithm was developed, consolidated, deployed and experimented on two inbound marketing channels.FindingsResearch examining machine learning implementation and operationalisation within the large consumer base is seemingly silent. This paper bridges this gap by developing and implementing a machine learning adaptive engine across two inbound marketing channels. The performance of the inbound channels revealed the significant importance of digital campaigns that are driven by machine learning algorithms. Machine learning techniques can be well positioned as an integral part of a large consumer base marketing operations with real-time one-to-one marketing capability.Research limitations/implicationsThe study explores the use of machine learning, a cutting-edge subset of artificial intelligence (AI), to drive consumer business revenue across different marketing channels. Further research should explore these marketing channels in greater depth by considering other branches of AI in driving consumer business revenue.Practical implicationsThis study demonstrates the value, ease and application of a machine learning deployment in a consumer business with a large customer base in driving business revenue. It also shows customers' practical response to offerings across channels and the importance of the digital channel to firms with a large customer base.Originality/valueThe paper defines how machine learning extracts can be deployed and operationalised by marketers to drive business revenue. This approach is unique, realistic, easy to deploy and will guide future research in this space.
Barriers to innovation in Brazilian small- and medium-sized enterprises
Authors: Silva Deoclécio Junior Cardoso da , Matos Guilherme Paraol de , Gibbon Artur Roberto de Oliveira , Veiga Claudimar Pereira da , Teixeira Clarissa Stefani , Lopes Luis Felipe Dias , Pique Josep Miquel 
Published: January 2025

PurposeThis research investigates the barriers impeding innovation within small- and medium-sized enterprises (SMEs) in Brazil, exploring 54 innovation-related barriers categorized into six distinct groups to offer substantial insights and analyses pertinent to the decision-makers, researchers and SMEs.Design/methodology/approachThis research employed a mixed quantitative and exploratory approach, utilizing fuzzy Delphi, fuzzy analytic hierarchy process (AHP) and fuzzy decision-making trial and evaluation laboratory (DEMATEL) methods. The fuzzy Delphi method confirmed the categories and barriers through quantitative analysis, the fuzzy AHP ranked the validated obstacles and the fuzzy DEMATEL method identified causal connections among the top-priority barriers.FindingsOut of 54 barriers, 23 significantly impacted SMEs. The “Financing and Financial” category was the most significant barrier, with “Access to Financing” being the most critical impediment. The barrier with the most influence was “Instability of Fiscal Policies,” and the highest causal priority was “Survival of the Priority Business,” identifying the government’s unstable fiscal policy as the principal barrier confronting SMEs in Brazil.Originality/valueThe primary challenges for Brazilian SMEs center on financing, fiscal policies and maintaining ongoing operations. By addressing these barriers and fostering a resilient business environment, SMEs’ innovation capabilities and competitiveness can be enhanced, serving as key drivers for sustainable economic growth in fluctuating economic conditions. This study contributes to the literature by highlighting and validating the main barriers to SME innovation, providing highly relevant information about the innovation process.
Process capability analysis of additive manufacturing process: a machine learning−based predictive model
Authors: Abdolahi Alireza , Soroush Hossein , Khodaygan Saeed 
Published: January 2025

Purpose
Predicting dimensional and geometrical errors in 3D printing parts during the design stage can significantly enhance the product’s quality. This study aims to predict the form deviation and process capability in additive manufacturing (AM) specimens considering layer thickness, laser power and scan speed parameters in the laser powder bed fusion (LPBF) method. Various machine learning (ML) techniques are implemented to estimate the form deviation and process capability with the highest accuracy in 3D-printed cylindrical parts as a case study.

Design/methodology/approach
The workflow started by simulating the LPBF AM process using a finite element modeling approach. Then, different ML algorithms like artificial neural networks are used to predict the form deviation. The process capability value is forecasted using some classification ML models and process capability indices (PCIs) for cylindrical parts. Finally, concentricity tolerance classification is performed for cylindrical parts, which can ensure quality control issues in the production stage.

Findings
Results present an accuracy of about 93% for predicting form deviations and 95% accuracy for predicting PCI C_pm in PCI classification based on random forest model as an ML algorithm.

Originality/value
The noteworthy point of the research is accessing the form deviation due to AM and process capability evaluation in the AM process before the production stage, which has not been studied before based on the author’s knowledge. So that the product quality is evaluated based on the shape deviation and its tolerances in the AM process digital chain.
Treatment of facial reactive B‐cell rich lymphoid proliferation with dapsone
Authors: Sugaya Makoto , Nagashima Akari , Nakamura Risae , Yano Megumi , Morita Takashi , Niijima Yasuko , Takekoshi Tomonori , Kanda Naoko , Tanaka Masaru 
Published: January 2025

AbstractReactive B‐cell rich lymphoid proliferation (rB‐LP), once called pseudolymphoma, is a benign lymphoproliferative disorder, which is histologically characterized by dense dermal infiltrate forming reactive germinal centers with tingible body macrophages. The disease frequently involves the face, scalp, and ear lobes. Treatment includes topical or intralesional steroids, surgical resection, ultraviolet therapy, laser treatment, and radiation therapy. Cases with multiple skin lesions, however, are difficult to treat with local therapies. Oral steroids are sometimes used, although the variety of side effects and recurrence during decreases in the dosage are the main problem. Here we report seven cases of rB‐LP treated with dapsone, which has anti‐inflammatory effect. Although the number of cases was limited, there was a trend that patients with multiple nodules on both sides of the face showed a good response. The limitations of this study include it being a case series from a single institute, and possible effects of topical steroids. We can, however, safely say that dapsone may be useful in some patients with rB‐LP.
ATL_FISHREF: A 12S mitochondrial reference dataset for metabarcoding Atlantic fishes frequently caught during scientific surveys in the Bay of Biscay
Authors: Rozanski Romane , Eme David , Richards Eilish , Deschez Marie‐Emilie , Rufino Marta M. , Trenkel Verena M. , Lorance Pascal , Marques Virginie , Spitz Jerôme , Cornette Florence , Albouy‐Boyer Séverine , Polanco Fernandez Andrea , Ernande Bruno , Pellissier Loic , Albouy Camille 
Published: January 2025

AbstractThe biodiversity crisis driven by anthropogenic pressures significantly threatens marine ecosystems. The rate of climate change and anthropogenic impacts outpace our traditional observation tools' capabilities, underscoring the urgency for new assessment methods. Environmental DNA (eDNA; DNA traces released by organisms) metabarcoding, a non‐invasive method widely developed over the last decade, represents a promising biomonitoring tool thanks to a large spatio‐temporal coverage, high detection of rare species and its time and cost‐effectiveness. However, capturing fish diversity using eDNA requires genetic reference databases, currently lacking. Improving reference databases relies on opportunistic sampling enabling the reporting of sequences for new species. The data provided here consists of barcoding 86 species of fishes over the 12S mitochondrial DNA gene. We generated 156 sequences of the mitochondrial 12S gene adapted to the “Teleo” barcodes from fishes sampled in the Bay of Biscay (BoB; Northeast Atlantic, France) between 2017 and 2019. In addition, we provided each individual the barcode details (Genbank accession number, chromatograms), a photograph, 5 ecomorphological measures and 11 life‐history traits documenting ecological functions (e.g., dispersion, habitat use, diet). Furthermore, we provided the sampling metadata (e.g., date, time, gear, coordinates, depth) and environmental variables measured in situ (e.g., conductivity, water/air temperature). This data set is valuable to improve the Northeast Atlantic eDNA genetic database, thus helping to better understand the effects of environmental forcing in the BoB, a transition zone housing mixed assemblages of boreal, temperate, and subtropical fish species susceptible to display variability in functional traits to adapt to changing conditions. The detailed Metadata for this abstract published in the Data Article section of the journal is available in MetaCat in JaLTER at https://jalter.diasjp.net/data/ERDP-2024-09.
Feeling and Healing in Different Spaces and Phases: How Individuals Coped With the Pandemic Cognitively, Affectively, and Conatively
Authors: Kim Sungsu , Jin Yan , Valentini Chiara , Choi Sung In , Badham Mark , Colleoni Elanor , Romenti Stefania 
Published: January 2025

ABSTRACTThis research investigates individuals' reactions and coping strategies during a prolonged health crisis over distinct temporal phases (i.e., early and late stages of the pandemic) and geographical locations (i.e., Australia, Finland, Italy, South Korea, Sweden, and the United States). Using the infectious disease threat (IDT) appraisal model as a guide, we conducted two separate studies at different intervals to investigate the coping strategies individuals utilised in response to the COVID‐19 pandemic. Findings revealed cross‐country differences in cognitive (i.e., COVID‐19 risk perception and vaccine uncertainty), affective (i.e., hope, fear, shame, and anger), and conative (i.e., preventive behavioural intentions, vaccination intentions, and preventive behavioural engagement) outcomes, shaped by cultural and policy influences. Mediation and moderation analyses showed that the coping mechanism of cognitive appraisal indirectly influenced conative coping through affective responses, which could be contingent on pandemic fatigue. This research contributes to crisis and health communication literature by offering a more nuanced understanding of how individuals across different countries cope with and manage a long‐lasting public health crisis and provides key takeaways for advancing pandemic communication theory and practice.
Consensus on Symptom Selection for Endometriosis Questionnaires: A Modified e‐Delphi Study
Authors: Zhu Tong , Marschall Henrik , Hansen Karina E. , Horne Andrew W. , Saraswat Lucky , Zondervan Krina T. , Missmer Stacey A. , Hummelshoj Lone , Bokor Atilla , Østrup Camilla S. , Melgaard Anna , Rytter Dorte 
Published: January 2025

ABSTRACTObjectiveTo build consensus on most important symptoms and related consequences for use in questionnaires to characterise individuals with suspected and confirmed endometriosis in the general population.DesignA questionnaire of 107 symptoms and related consequences of endometriosis was collaboratively developed by patients, medical doctors and researchers and further assessed in a two‐round e‐Delphi study. Participants assessed the relevance of the symptoms, and a priori it was decided that 70% was the threshold for inclusion of a symptom.SettingParticipants represented 7 countries, including Australia, Denmark, France, Hungary, the United Kingdom, the United States, and Turkey.PopulationIndividuals with endometriosis, medical doctors and researchers with expertise in endometriosis.MethodsA modified e‐Delphi study.Main Outcome MeasuresConsensus‐based selection of symptoms for endometriosis questionnaires.ResultsSeventy‐six participants completed the first Delphi round and 65 completed the second round. Four symptoms met consensus in the first round (menstrual pain, pain during sexual intercourse, cyclic pain during defecation, and infertility), with two additional symptoms reaching consensus in the second round (cyclic pain and increased doctor/health care contacts for abdominal/pelvic pain).ConclusionThis study highlighted six symptoms relevant for inclusion in endometriosis research questionnaires: menstrual pain, pain during sexual intercourse, cyclic pain during defecation, cyclic pain, infertility, and a high number of doctor/health care visits due to abdominal/pelvic pain. Recognising a broad range of potential symptoms is essential for raising awareness and supporting early detection efforts.
L‐Arginine and L‐Citrulline for Prevention and Treatment of Pre‐Eclampsia: A Systematic Review and Meta‐Analysis
Authors: Makama Maureen , McDougall Annie R. A. , Cao Jenny , Mills Kate , Nguyen Phi‐Yen , Hastie Roxanne , Ammerdorffer Anne , Gülmezoglu A. Metin , Vogel Joshua P. 
Published: January 2025

ABSTRACTBackgroundEvidence suggests L‐arginine may be effective at reducing pre‐eclampsia and related outcomes. However, whether L‐arginine can prevent or only treat pre‐eclampsia, and thus the target population and timing of initiation, remains unknown.ObjectivesTo evaluate the effects of L‐arginine and L‐citrulline (precursor of L‐arginine) on the prevention and treatment of pre‐eclampsia.Search StrategyMEDLINE, Embase, CINAHL, Global Index Medicus and the Cochrane Library were searched through 7 February 2024.Selection CriteriaTrials administering L‐arginine or L‐citrulline to pregnant women, with the comparison group receiving placebo or standard care, were included.Data Collection and AnalysisMeta‐analyses were conducted separately for prevention or treatment trials, using random‐effects models.Main ResultsTwenty randomised controlled trials (RCTs) (2028 women) and three non‐randomised trials (189 women) were included. The risk of bias was ‘high’ in eight RCTs and showed ‘some concerns’ in 12. In prevention trials, L‐arginine was associated with a reduced risk of pre‐eclampsia (relative risk [RR] 0.52; 95% confidence interval [CI], 0.35, 0.78; low‐certainty evidence, four trials) and severe pre‐eclampsia (RR 0.23; 95% CI, 0.09, 0.55; low‐certainty evidence, three trials). In treatment trials, L‐arginine may reduce mean systolic blood pressure (MD −5.64 mmHg; 95% CI, −10.66, −0.62; very low‐certainty evidence, three trials) and fetal growth restriction (RR 0.46; 95% CI, 0.26, 0.81; low‐certainty evidence, two trials). Only one study (36 women) examined L‐citrulline and reported no effect on pre‐eclampsia or blood pressure.ConclusionsL‐arginine may be promising for pre‐eclampsia prevention and treatment, but findings should be interpreted cautiously. More trials are needed to determine the optimal dose and time to commence supplementation and support clinical decision‐making.
Are Geopolitical Risk and Economic Uncertainty Matter for Natural Resources Rents?
Authors: Öge Güney Pelin 
Published: January 2025

ABSTRACTThe aim of this study is to investigate the impact of geopolitical risk and economic policy uncertainty on natural resource rents for selected MENA countries and Turkey, covering the period 1973–2021. The geopolitical risk index was researched by Caldara and Iacoviello and the World Uncertainty Index was taken from Ahir et al. We used linear and nonlinear ARDL techniques. Results from linear ARDL models document that geopolitical risk has a significant positive effect on natural resource rents in Egypt and Turkey, while it has a negative effect on natural resource rents in Saudi Arabia and Tunisia. When we look at the impact of uncertainty on natural resource rents, we see that increasing uncertainty reduces natural resource rents in all countries except Saudi Arabia. Moreover, the findings of nonlinear ARDL models show that negative and positive shocks in the geopolitical risk and uncertainty index affect natural resource rents in both the short and long term. Our results show that geopolitical risk and economic policy uncertainty can be used to improve models of natural resource markets.