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The growing need for sustainable energy practices necessitates technology-driven interventions that can effectively bridge the disparity between consumer intentions and actual behavior. This paper formulates and empirically substantiates an IoT-enabled digital nudge architecture designed to promote sustainable energy behavior. The architecture provides goal-setting, social comparison, feedback, and informational nudges across multiple digital channels, utilizing linked devices, data processing layers, and a rule-based nudge engine. An 815-responder survey was analyzed using structural equation modeling with partial least squares (SEM-PLS) to identify the drivers of sustainable energy behavior and explore technology readiness as a moderating factor. The results show that nudges utilizing the Internet of Things (IoT) significantly enhance the alignment between intention and behavior. Goal-setting and feedback mechanisms have the highest effects. The findings also demonstrate that being ready for new technology improves nudge response, highlighting the importance of user-centered system design. This paper presents a scalable infrastructure for integrating IoT into sustainability projects, as well as theoretical contributions to technology adoption and behavioral intervention research. The study enhances the dialogue on environmental technology by illustrating the implementation of digital nudges through IoT infrastructures to expedite progress toward the Sustainable Development Goals (SDGs). Full article

(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)

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19 pages, 1738 KB

Open AccessArticle

Design and Implementation of a Smart Parking System with Real-Time Slot Detection and Automated Gate Access

by Mohammad Ali Sahraei

Technologies 2025, 13(11), 503; https://doi.org/10.3390/technologies13110503 (registering DOI) - 1 Nov 2025

Abstract

By increasing the number of vehicles, an intelligent parking system can help drivers in finding parking slots by providing real-time information. To address this issue, this study developed an Arduino-based automated parking system integrating sensors to assist drivers in quickly discovering available parking slots with real-time space detection and dynamic access control. This system consists of ultrasonic sensors, NodeMCU, an LCD screen, a servo motor, and an Arduino Uno. Each ultrasonic sensor is assigned a specific number corresponding to its slot number, which helps to identify the locations. These sensors were connected to the NodeMCU to collect, process, and transfer data to the Arduino board. If the ultrasonic sensor cannot detect the vehicle in the parking space, the LCD screen will show the number of specific slots. The Arduino will use the servo motor to open the entrance gate if a vehicle is detected by another ultrasonic sensor next to it. Otherwise, the system prevents any vehicle from entering the parking area when all of the available spaces are occupied. The system prototype is constructed and empirically evaluated to verify its performance and efficiency. The results indicate that the system successfully monitors parking spot occupancy and validates its capacity for real-time information updates. Full article

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22 pages, 9577 KB

Open AccessArticle

YOLOv11-4ConvNeXtV2: Enhancing Persimmon Ripeness Detection Under Visual Challenges

by Bohan Zhang, Zhaoyuan Zhang and Xiaodong Zhang

AI 2025, 6(11), 284; https://doi.org/10.3390/ai6110284 (registering DOI) - 1 Nov 2025

Abstract

Reliable and efficient detection of persimmons provides the foundation for precise maturity evaluation. Persimmon ripeness detection remains challenging due to small target sizes, frequent occlusion by foliage, and motion- or focus-induced blur that degrades edge information. This study proposes YOLOv11-4ConvNeXtV2, an enhanced detection framework that integrates a ConvNeXtV2 backbone with Fully Convolutional Masked Auto-Encoder (FCMAE) pretraining, Global Response Normalization (GRN), and Single-Head Self-Attention (SHSA) mechanisms. We present a comprehensive persimmon dataset featuring sub-block segmentation that preserves local structural integrity while expanding dataset diversity. The model was trained on 4921 annotated images (original 703 + 6 × 703 augmented) collected under diverse orchard conditions and optimized for 300 epochs using the Adam optimizer with early stopping. Comprehensive experiments demonstrate that YOLOv11-4ConvNeXtV2 achieves 95.9% precision and 83.7% recall, with mAP@0.5 of 88.4% and mAP@0.5:0.95 of 74.8%, outperforming state-of-the-art YOLO variants (YOLOv5n, YOLOv8n, YOLOv9t, YOLOv10n, YOLOv11n, YOLOv12n) by 3.8–6.3 percentage points in mAP@0.5:0.95. The model demonstrates superior robustness to blur, occlusion, and varying illumination conditions, making it suitable for deployment in challenging maturity detection environments. Full article

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32 pages, 1289 KB

Open AccessReview

Soil Pollution Mapping Across Africa: Potential Tool for Soil Health Monitoring

by Georges K. Kome, Caroline A. Kundu, Michael A. Okon, Roger K. Enang, Samuel A. Mesele, Julius Opio, Eric Asamoah and Chrow Khurshid

Pollutants 2025, 5(4), 38; https://doi.org/10.3390/pollutants5040038 (registering DOI) - 1 Nov 2025

Abstract

There is an urgent need for an updated and relevant soil information system (SIS) to sustainably use and manage the land across Africa. Accurate data on soil pollution is essential for effective decision-making in soil health monitoring and management. Unfortunately, the data and information are not usually presented in formats that can easily guide decision-making. The objectives of this work were to (i) assess the availability of soil pollution maps, (ii) evaluate the methodologies used in creating these maps, (iii) explore the role of soil pollution maps in soil health monitoring, and (iv) identify gaps and challenges in soil pollution mapping in Africa. Soil pollution maps across Africa are created on a local scale, with highly variable sampling size and low sampling density. The most used mapping techniques include spatial interpolation (kriging and inverse distance weighting). Among the types of soil pollutants mapped, heavy metals have received priority, while pesticides and persistent organic pollutants have received less attention. Soil pollution mapping is not incorporated within the SIS framework due to lack of reliable spatially comprehensive data and technological and institutional barriers. Current efforts remain fragmented, site-specific, and methodologically inconsistent, resulting in significant data gaps that hinder reliable monitoring and limit progress in soil pollution mapping. Full article

(This article belongs to the Special Issue The Effects of Global Anthropogenic Trends on Ecosystems, 2025)

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34 pages, 5251 KB

Open AccessArticle

AI-Based Sentiment Analysis of E-Commerce Customer Feedback: A Bilingual Parallel Study on the Fast Food Industry in Turkish and English

by Esra Kahya Özyirmidokuz, Bengisu Molu Elmas and Eduard Alexandru Stoica

J. Theor. Appl. Electron. Commer. Res. 2025, 20(4), 294; https://doi.org/10.3390/jtaer20040294 (registering DOI) - 1 Nov 2025

Abstract

Across digital platforms, large-scale assessment of customer sentiment has become integral to brand management, service recovery, and data-driven marketing in e-commerce. Still, most studies center on single-language settings, with bilingual and culturally diverse environments receiving comparatively limited attention. In this study, a bilingual sentiment analysis of consumer feedback on X (formerly Twitter) was conducted for three global quick-service restaurant (QSR) brands—McDonald’s, Burger King, and KFC—using 145,550 English tweets and 15,537 Turkish tweets. After pre-processing and leakage-safe augmentation for low-resource Turkish data, both traditional machine learning models (Naïve Bayes, Support Vector Machines, Logistic Regression, Random Forest) and a transformer-based deep learning model, BERT (Bidirectional Encoder Representations from Transformers), were evaluated. BERT achieved the highest performance (macro-F1 ≈ 0.88 in Turkish; ≈0.39 in temporally split English), while Random Forest emerged as the strongest ML baseline. An apparent discrepancy was observed between pseudo-label agreement (Accuracy > 0.95) and human-label accuracy (EN: 0.75; TR: 0.49), indicating the limitations of lexicon-derived labels and the necessity of human validation. Beyond methodological benchmarking, linguistic contrasts were identified: English tweets were more polarized (positive/negative), whereas Turkish tweets were overwhelmingly neutral. These differences reflect cultural patterns of online expression and suggest direct managerial implications. The findings indicate that bilingual sentiment analysis yields brand-level insights that can inform strategic and operational decisions. Full article

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25 pages, 458 KB

Open AccessArticle

Shifting Perceptions and Behaviors: The Impact of Digitalization on Banking Services

by Alina Elena Ionașcu, Vlad I. Bocanet, Nicoleta Asaloș, Cristina Mihaela Lazăr, Elena Cerasela Spătariu, Corina Aurora Barbu and Dorinela Nancu

J. Theor. Appl. Electron. Commer. Res. 2025, 20(4), 295; https://doi.org/10.3390/jtaer20040295 (registering DOI) - 1 Nov 2025

Abstract

The rapid digitalization of banking services has transformed consumer interactions, necessitating a deeper understanding of the factors influencing online banking adoption. This research investigates the factors influencing consumer adoption in a country undergoing rapid digital transformation but still facing gaps in digital skills and infrastructure—Romania. The objective of the study is to analyze how key variables such as ease of use, security, speed, usefulness, and social pressure influence online banking behavior of Romanian consumers, especially the most digitally engaged ones. The study utilizes a multi-method empirical approach, hypothesis testing, binary logistic regression for prediction modeling, and segmentation analysis to offer a comprehensive view of customer behavior. The findings identify essential adoption drivers and separate customer profiles, providing useful information for financial organizations aiming to enhance their digital strategy. Perceived ease of use and perceived security are primary factors influencing adoption; nevertheless, decision tree analysis indicates that speed and usefulness have a more significant impact than logistic regression implies, but social pressure unexpectedly serves as an impediment. These results highlight the necessity for banks to customize their digital services, harmonizing security and user-friendliness with improved efficiency and usefulness to promote broader adoption in emerging digital economies like Romania. Full article

(This article belongs to the Topic Artificial Intelligence Applications in Financial Technology, 2nd Edition)

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16 pages, 650 KB

Open AccessReview

Integrating Dentistry into Interprofessional Healthcare: A Scoping Review on Advancing Collaborative Practice and Patient Outcomes

by Man Hung, Wendy C. Birmingham, Madeleine Tucker, Connor Schwartz and Amir Mohajeri

Healthcare 2025, 13(21), 2780; https://doi.org/10.3390/healthcare13212780 (registering DOI) - 1 Nov 2025

Abstract

Background: Interprofessional collaboration is vital for comprehensive, patient-centered care. Despite growing recognition of oral–systemic health links, the integration of dentists into healthcare teams remains limited. This scoping review mapped existing evidence on dental professionals’ roles within interprofessional healthcare, identifying key benefits, barriers, and facilitators. Methods: A systematic search of PubMed, SCOPUS, and Web of Science identified English-language studies (2014 to 2024) focused on collaboration between dental and non-dental providers. Studies addressing oral–systemic health without team-based integration were excluded. Screening and data charting followed the PRISMA-ScR framework using JBI data extraction and critical appraisal tools. Data were synthesized thematically by collaboration model, outcomes, and influencing factors. Results: Nine studies met the inclusion criteria. Integrating dental professionals into healthcare teams improved patient outcomes, quality of life, and satisfaction. Effective models included nurse practitioner–dentist partnerships and medical–dental collaboration in pediatrics and chronic disease care. Barriers included poor communication, lack of interoperable electronic health records, role ambiguity, and limited interprofessional training. Key facilitators were supportive policies, integrated care structures, professional education, and strong team communication. Conclusions: Integrating dentists into interprofessional teams enhances healthcare delivery and patient outcomes. However, significant barriers remain. Addressing communication gaps, implementing shared health records, and expanding interprofessional education are essential steps toward more cohesive care. Future research should evaluate scalable integration frameworks and incorporate patient perspectives to inform team-based care. Full article

(This article belongs to the Special Issue Oral and Maxillofacial Health Care: Third Edition)

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12 pages, 977 KB

Open AccessArticle

Molecular Surveillance of Pyrethroid Resistance Kdr Alleles T917I and L920F in Head and Body Lice from Nigeria

by Joshua Kamani, Shimon Harrus, Bukar Laminu, Yaarit Nachum-Biala, Mike Shand, Gonzalo Roca-Acevedo and Ariel Ceferino Toloza

Parasitologia 2025, 5(4), 57; https://doi.org/10.3390/parasitologia5040057 (registering DOI) - 1 Nov 2025

Abstract

Pediculosis produced by the presence of the human head louse (Pediculus humanus capitis DeGeer, 1767) and the body louse (Pediculus humanus humanus L., 1758) remains a neglected tropical disease in Nigeria, where permethrin-based pediculicides are widely used. However, the resistance status of lice populations has not been previously assessed. Knockdown resistance (kdr) to pyrethroids is primarily driven by two mutations—T917I and L920F—in the voltage-sensitive sodium channel (VSSC) gene. This study investigated the presence of these mutations in 85 head and body lice collected from school-age children in two settlements in Nigeria. The T917I mutation was detected in head lice at frequencies ranging from 21% to 76%, and in body lice from 10% to 95%, with significant variation between sites and louse types. Remarkably, all lice examined carried the L920F mutation, regardless of T917I genotype, a pattern not previously reported in body lice. These findings suggest that pyrethroid resistance is well established or under active selection in the study populations. This is the first report of kdr mutations in human lice from Nigeria and highlights the urgent need for resistance monitoring programs. Early genetic surveillance of these mutations can inform treatment strategies and help prevent widespread resistance in lice populations, preserving the efficacy of available pediculicides. Full article

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38 pages, 3209 KB

Open AccessArticle

Toward a Coherent AI Literacy Pathway in Technology Education: Bibliometric Synthesis and Cross-Sectional Assessment

by Denis Rupnik and Stanislav Avsec

Educ. Sci. 2025, 15(11), 1455; https://doi.org/10.3390/educsci15111455 (registering DOI) - 1 Nov 2025

Abstract

Rapid advances in artificial intelligence (AI) are reshaping curricula and work, yet technology and engineering education lack a coherent, critical AI literacy pathway. In this study, we (1) mapped dominant themes and intellectual bases and (2) compared AI literacy between secondary technical students and pre-service technology and engineering teachers to inform curriculum design. Moreover, we conducted a Web of Science bibliometric analysis (2015–2025) and derived a four-pillar framework (Foundational Knowledge, Critical Appraisal, Participatory Design, and Pedagogical Integration) of themes consolidated around GenAI/LLMs and ethics, with strong growth (1259 documents, 587 sources). Phase 2 was a cross-sectional field study (n = 145; secondary n = 77, higher education n = 68) using the AI literacy test. ANOVA showed higher total scores for pre-service teachers than secondary technical students (p = 0.02) and a sex effect favoring males (p = 0.01), with no interaction. MANCOVA found no multivariate group differences across 14 competencies, but univariate advantages for pre-service technology teachers were found in understanding intelligence (p = 0.002) and programmability (p = 0.045); critical AI literacy composites did not differ by group, while males outperformed females in interdisciplinarity and ethics. We conclude that structured, performance-based curricula aligned to the framework—emphasizing data practices, ethics/governance, and human–AI design—are needed in both sectors, alongside measures to close gender gaps. Full article

(This article belongs to the Special Issue Technology-Enhanced Education for Engineering Students)

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26 pages, 15315 KB

Open AccessArticle

Machine and Deep Learning Framework for Sargassum Detection and Fractional Cover Estimation Using Multi-Sensor Satellite Imagery

by José Manuel Echevarría-Rubio, Guillermo Martínez-Flores and Rubén Antelmo Morales-Pérez

Data 2025, 10(11), 177; https://doi.org/10.3390/data10110177 (registering DOI) - 1 Nov 2025

Abstract

Over the past decade, recurring influxes of pelagic Sargassum have posed significant environmental and economic challenges in the Caribbean Sea. Effective monitoring is crucial for understanding bloom dynamics and mitigating their impacts. This study presents a comprehensive machine learning (ML) and deep learning (DL) framework for detecting Sargassum and estimating its fractional cover using imagery from key satellite sensors: the Operational Land Imager (OLI) on Landsat-8 and the Multispectral Instrument (MSI) on Sentinel-2. A spectral library was constructed from five core spectral bands (Blue, Green, Red, Near-Infrared, and Short-Wave Infrared). It was used to train an ensemble of five diverse classifiers: Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), a Multi-Layer Perceptron (MLP), and a 1D Convolutional Neural Network (1D-CNN). All models achieved high classification performance on a held-out test set, with weighted F1-scores exceeding 0.976. The probabilistic outputs from these classifiers were then leveraged as a direct proxy for the sub-pixel fractional cover of Sargassum. Critically, an inter-algorithm agreement analysis revealed that detections on real-world imagery are typically either of very high (unanimous) or very low (contentious) confidence, highlighting the diagnostic power of the ensemble approach. The resulting framework provides a robust and quantitative pathway for generating confidence-aware estimates of Sargassum distribution. This work supports efforts to manage these harmful algal blooms by providing vital information on detection certainty, while underscoring the critical need to empirically validate fractional cover proxies against in situ or UAV measurements. Full article

(This article belongs to the Section Spatial Data Science and Digital Earth)

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26 pages, 1512 KB

Open AccessArticle

Pulse-Driven Spin Paradigm for Noise-Aware Quantum Classification

by Carlos Riascos-Moreno, Andrés Marino Álvarez-Meza and German Castellanos-Dominguez

Computers 2025, 14(11), 475; https://doi.org/10.3390/computers14110475 (registering DOI) - 1 Nov 2025

Abstract

Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potential to exploit high-dimensional feature maps, entanglement-enabled correlations, and non-classical priors. Yet, practical realizations remain constrained by the Noisy Intermediate-Scale Quantum (NISQ) era, where limited qubit counts, gate errors, and coherence losses necessitate frugal, noise-aware strategies. The Data Re-Uploading (DRU) algorithm has emerged as a strong NISQ-compatible candidate, offering universal classification capabilities with minimal qubit requirements. While DRU has been experimentally demonstrated on ion-trap, photonic, and superconducting platforms, no implementations exist for spin-based quantum processing units (QPU-SBs), despite their scalability potential via CMOS-compatible fabrication and recent demonstrations of multi-qubit processors. Here, we present a pulse-level, noise-aware DRU framework for spin-based QPUs, designed to bridge the gap between gate-level models and realistic spin-qubit execution. Our approach includes (i) compiling DRU circuits into hardware-proximate, time-domain controls derived from the Loss–DiVincenzo Hamiltonian, (ii) explicitly incorporating coherent and incoherent noise sources through pulse perturbations and Lindblad channels, (iii) enabling systematic noise-sensitivity studies across one-, two-, and four-spin configurations via continuous-time simulation, and (iv) developing a noise-aware training pipeline that benchmarks gate-level baselines against spin-level dynamics using information-theoretic loss functions. Numerical experiments show that our simulations reproduce gate-level dynamics with fidelities near unity while providing a richer error characterization under realistic noise. Moreover, divergence-based losses significantly enhance classification accuracy and robustness compared to fidelity-based metrics. Together, these results establish the proposed framework as a practical route for advancing DRU on spin-based platforms and motivate future work on error-attentive training and spin–quantum-dot noise modeling. Full article

20 pages, 1780 KB

Open AccessArticle

A Social Survey to Capture the Public Awareness and Perception About Chemicals Under Ireland’s Human Biomonitoring Feasibility Study

by Richa Singh, Holger Martin Koch, Marike Kolossa-Gehring, André Conrad and Alison Connolly

Environments 2025, 12(11), 410; https://doi.org/10.3390/environments12110410 (registering DOI) - 1 Nov 2025

Abstract

As chemical exposures are increasingly emphasised as public health concerns, understanding how people perceive chemical risks is vital for shaping responsive and inclusive human biomonitoring (HBM) programmes. Public awareness not only influences individual behaviours but can also inform national policy priorities and scientific focus. This study reports findings from the Human Biomonitoring for Ireland (HBM4IRE) feasibility study, which conducted a social survey adapted from the HBM4EU framework. The survey assessed awareness and perceived harmfulness of 24 chemical groups among 218 Irish residents, distinguishing between experts (involved in chemical management) and non-experts. Lead, arsenic, mercury, pesticides, tobacco alkaloids, volatile organic compounds (VOCs), solvents, cadmium, polycyclic aromatic hydrocarbons (PAHs), and persistent organic pollutants (POPs) received the highest perceived harmfulness scores. Non-experts reported lower perceived harmfulness for substances such as phthalates, parabens, and Per- and polyfluoroalkyl substances (PFASs), indicating significant awareness gaps. These findings demonstrate convergence between public and expert views for well-recognised substances but also highlight gaps for certain emerging chemicals. This study highlights the importance of targeted, country-specific education campaigns and shows the added value of integrating public perceptions into HBM design and priority setting. Full article

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14 pages, 288 KB

Open AccessArticle

Factors Associated with Missed Opportunities for Vaccination in Children During the First Year of Life: A Cross-Sectional Study

by Wágnar Silva Morais Nascimento, Eugênio Barbosa de Melo Júnior, Ana Raisla de Araújo Rodrigues, Beatriz Mourão Pereira, Joaquim Guerra de Oliveira Neto, Paulo de Tarso Moura Borges, Antonio Rosa de Sousa Neto and Telma Maria Evangelista de Araújo

Vaccines 2025, 13(11), 1129; https://doi.org/10.3390/vaccines13111129 (registering DOI) - 1 Nov 2025

Abstract

Background: Addressing Missed Opportunities for Vaccination (MOV) contributes to increased vaccination rates in children, reinforcing the need to investigate and intervene in the related factors. Objective: To analyze factors associated with missed opportunities for vaccination in children under one year of age in a Brazilian capital. Methods: This was a cross-sectional, analytical study conducted in seven Basic Health Units in Teresina, Piauí, Brazil. A previously validated questionnaire was applied to parents or guardians of a sample of 316 children. Data were collected from March to June 2025. Multivariable Logistic Regression was performed, and results were expressed as Odds Ratios. Results: Among the children, 53.5% had at least one MOV. The associated factors were: parents with two or more children (95% CI: 1.06–2.96), false contraindications (95% CI: 1.29–8.73), inadequate assessment of vaccination cards by health professionals (95% CI: 1.78–29.00), vaccine shortages in health units (95% CI: 1.57–18.28), and refusal to open multidose vaccine vials (95% CI: 1.81–19.31). Receiving information about vaccination in the previous month was a protective factor against MOV (95% CI: 0.25–0.77). The vaccines most frequently contributing to MOV were BCG (15.8%) and the COVID-19 vaccine, with 15.5% for the first dose and 14.9% for the second. Conclusions: The high prevalence of MOV found in this study indicates weaknesses in the immunization process and suggests the need for implementing measures to interrupt the chain of causes leading to MOV, thereby contributing to the achievement of the objectives of the Brazilian National Immunization Program. Full article

(This article belongs to the Special Issue The Role of Vaccination on Public Health and Epidemiology)

13 pages, 6722 KB

Open AccessArticle

Peripheral Blood Gene Expression Profiling in Proliferative Diabetic Retinopathy Using NanoString Technology

by Alon Zahavi, Shirel Weiss, Jawad Abu Dbai, Talal Salti and Nitza Goldenberg-Cohen

Diabetology 2025, 6(11), 132; https://doi.org/10.3390/diabetology6110132 (registering DOI) - 1 Nov 2025

Abstract

Background: Proliferative diabetic retinopathy (PDR) is a vision-threatening complication of diabetes characterized by retinal neovascularization. Predicting which diabetic patients will develop PDR remains challenging. Measuring mRNA expression levels may help elucidate the molecular pathways involved in PDR pathogenesis. This study investigated the expression of genes related to inflammatory and proliferative pathways in the peripheral blood of patients with PDR, compared to patients with non-proliferative diabetic retinopathy (NPDR) and healthy controls, using NanoString technology. The findings may aid in identifying potential biomarkers and therapeutic targets for early intervention. Methods: This prospective study was approved by the institutional ethics review board, and written informed consent was obtained from all participants. The study included patients with PDR (n = 9), NPDR (n = 8), and non-diabetic controls (n = 6). Total RNA was extracted from whole blood samples using the MagNA Pure Compact RNA Isolation Kit (Roche Ltd., Basel, Switzerland) and analyzed with the NanoString platform (Agentek Ltd., Yakum, Israel). Results: Expression levels of 578 genes across 15 signaling pathways, including inflammation (e.g., IL-17, TNF, and NF-κB) and cancer-related PI3K-Akt pathways, were evaluated. Sixty-six genes (11.5%) were differentially expressed (p < 0.05) between the PDR group and the NPDR and control groups. The most prominently overexpressed genes in PDR included TGFβ1, TGFβ1R, IL23R, BAX, and CFB, which were primarily involved in inflammatory and proliferative signaling. Conclusions: Gene expression profiling using NanoString technology revealed significant upregulation of genes related to inflammation and proliferation in patients with PDR. These findings suggest that beyond angiogenesis, inflammatory and proliferative pathways may play a central role in PDR development and could serve as targets for novel therapeutic strategies. Full article

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16 pages, 657 KB

Open AccessStudy Protocol

A Grounded Theory of the Lived Experiences of People with Pancreatic Cancer in Northern Ireland: Study Protocol

by Lana Cook, Gillian Prue, Susan McLaughlin and Gary Mitchell

Healthcare 2025, 13(21), 2779; https://doi.org/10.3390/healthcare13212779 (registering DOI) - 1 Nov 2025

Abstract

Background/Objectives: Pancreatic cancer remains highly fatal, often diagnosed late with poor prognoses and worse psychological quality of life compared to other cancers. Globally, it is the twelfth most common cancer but the sixth leading cause of cancer-related deaths, with actual 5-year survival rates below 5%. Northern Ireland’s outcomes are among the worst, yet research on people’s experiences across the illness trajectory is scarce. Consequently, the unique needs of people with pancreatic cancer are poorly understood. It is crucial we develop deeper understanding of the entire pancreatic cancer journey to address this. This study aims to explore the lived experiences of people diagnosed with pancreatic cancer in Northern Ireland and generate a theory that explains their journeys, from pre-diagnosis through to survivorship or end of life. Methods: This study will adopt a grounded theory approach, incorporating multiple qualitative data generation methods: semi-structured interviews with patients and care partners, and focus groups with professionals. An optional photovoice (participatory photography) method will be offered to participants. Theoretical sampling principles and constant comparative analysis will guide recruitment, data collection, and analysis to ensure the explanatory theory is rooted in participants’ lived experiences. Conclusions: Establishing a holistic, in-depth understanding of people’s pancreatic cancer journeys will enable us to better comprehend, anticipate, and meet their needs. A theory grounded in empirical data about lived experiences can inform priorities for future care, support services, policy, and research, and contribute to the development of support interventions that help people to maintain the best possible quality of life, whether during a short-term, terminal illness; treatment journey; long-term symptom management; or survivorship. Full article

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