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Facilitators and barriers to early diagnosis of pleural mesothelioma: A qualitative study of patients’ experiences towards getting a diagnosis
Get PDFopen access articlePurpose: Prognosis with pleural mesothelioma (PM) is poor, yet evidence indicates a better chance of survival and quality of life if diagnosed earlier. There has been little attention to PM patients’ experiences prior to diagnosis as available studies have focused on their lived experiences after diagnosis. This study aims to qualitatively explore and identify the barriers and facilitators to PM diagnosis and looks to understand the reasons for any variability in patients’ experiences along their diagnosis journey and proposed treatment plans.
Methods: Seventeen participants with confirmed PM diagnosis took part in in-depth, semi-structured interviews about their journey to diagnosis. Participants were purposively recruited from two specialist PM outpatient clinics in England. The interview data were analysed using framework analysis underpinned by the Model of Pathway to treatment.
Results: Our findings identified 15 different barriers and facilitators across the four intervals within the model. Within the appraisal and diagnostic intervals, the presentation of vague symptoms that were mistaken for a less serious illness were a considerable barrier. Health literacy regarding PM had an impact on how soon a patients sought help regarding their symptoms and how quickly they were placed on a PM diagnostic pathway by the healthcare professionals (HCP), and this was impacted the HCP’s knowledge of PM.
Conclusion: Earlier symptom recognition by both patient and those in initial contact such as General practitioners (GPs) and other HCPs, can be used to target significant and avoidable delays along the pathway, thereby promoting earlier diagnosis and treatment options
Three-way decision-based consensus feedback mechanism considering conflict level and social network for large-scale group decision-making
No full textThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Large-scale group decision-making (LSGDM) has recently gained significant attention, with most studies viewing decision makers’ (DMs) behaviors as either supportive or non-supportive. However, as decision-making complexity increases, DMs encounter diverse situations, which introduces greater uncertainty into the decision process. The three-way decision theory provides a new perspective to address this problem by dividing the universal set into three regions: positive, boundary and negative, thereby reducing decision-making risks. Building on this, we propose a three-way decision-based consensus feedback mechanism (3WD-CFM) considering conflict level and social network for LSGDM, in which DMs are classified into three behaviors of support, uncertainty, and non-support by introducing a quantitative loss function, mitigating reliance on subjective prior knowledge. A targeted feedback strategy is developed that prioritizes non-supportive DMs and adapts adjustment rules according to behavioral classifications to enhance consensus efficiency. To jointly optimize global consensus and local subgroup harmony, a dual-layered control mechanism is introduced, incorporating conflict monitoring as a parallel indicator. Furthermore, a collaborative representation approach is employed to uncover latent relational structures among DMs, based on which a reputation-based weighting mechanism is constructed, balancing individual preferences with structural interdependencies. Finally, the validity and robustness of the 3WD-CFM are proved by numerical experiments and parameter analysis, and its superiority is highlighted through several comparisons
Self-Efficacy and Nontask Performance at Work. A Meta-Analytic Summary
Get PDFopen access articleSelf-effcacy plays a critical role in guiding and maintaining behaviours across various life domains, including organisational settings where it enhances task-specific performance. This paper extends the role of self-efficacy to non task or contextual performance, focusing on citizenship and counterproductive performance. Through a systematic review and meta-analysis, we examine its role as both an antecedent and a moderator. Among 11,877 records, 176 papers (194 independent studies) were included in the systematic review, and 158 papers (172 independent studies) in the meta-analysis. Findings support our hypotheses. In relation to citizenship performance (N = 49,464) results showed that self-efficacious individuals are more likely to engage in extra-role activities, fostering personal, collective, and organisational development (ρ = .45). They exhibit proactive behaviours such as voicing concerns, providing exceptional customer service, and helping behaviours. Additionally, self-efficacy serves as a protective factor against counterproductive and antisocial performance detrimental to organisations and stakeholders (N = 12,498, ρ = − .24). While studies on the moderation of self effcacy are limited, our systematic review confirms its role in buffering the impact of adverse working conditions on counterproductive performance
Competitive many-task differential evolution with reinforcement learning and meta-knowledge transfer
Get PDFCompetitive many-task optimization (CMaTO) is a special many-task optimization paradigm whose purpose is to find the best optimal solution for all tasks. However, the existing CMaTO algorithms perform poorly in the design of knowledge transfer from auxiliary tasks to the main task, resulting in a prolonged period of stagnant optimal fitness for the main task. To address these shortcomings, a competitive many-task optimization algorithm is proposed, based on reinforcement learning and meta-knowledge transfer, leveraging differential evolution as a foundational evolutionary strategy. This algorithm employs a reinforcement learning algorithm to select auxiliary tasks that can accelerate the convergence of the optimal value or jump out of the stagnation state according to the evolutionary state. Meanwhile, a stagnation detection operator is designed to switch the main task when the optimal value stagnation threshold upper limitation is reached. Furthermore, the meta-knowledge migration algorithm is embedded to judge the evolutionary state of the population based on the distance between the optimal solution and the centroid of the population. The migration radius is adaptively adjusted, and the knowledge is utilized to facilitate the evolution of high-quality solutions for the source task, which can assist the population in accelerating convergence or escaping a local optimum. To evaluate the performance of the proposed algorithm, three CMaTO benchmark test suites and a real-world Unmanned Aerial Vehicle (UAV) task allocation problem are chosen to compare it with other state-of-the-art strategies. The results show that the proposed algorithm achieved better performance
Changes in Health-Related Behaviours Among Adults Who Accessed Real-World Weight Management Support: 12-Month Outcomes
No full textopen access articleBackground
Large weight losses are desirable, but their benefits are short-lived without sustained behaviour changes that can be maintained at the household level. This longitudinal study, conducted in a real-life setting, investigated changes in weight, dietary habits, activity levels, and physical and mental well-being of members of a community weight management programme (Slimming World), compared with a matched cross-sectional reference group from the general population. The wider influence on the dietary and activity habits of family members was also explored.
Methods
Longitudinal data were collected from members at 0-4 weeks (T1), 3 months (T2), and 12 months (T4) after joining. The reference group completed surveys at each time point. Diet quality scores (NDQS) were calculated using a validated tool, hours of moderate-intensity physical activity were recorded, and mental well-being was assessed using adapted items from the SF Health Survey. Changes in members’ behaviours and comparisons with the reference group were analysed using within- and between-group ANOVAs with p-adjusted post-hoc comparisons.
Results
Of the 1,884 members who provided baseline data, 174 (7.5% male) completed surveys at T1, T2, and T4. At baseline, mean BMI and age were 34.7 ± 7.0 kg/m² and 53.0 ± 12.0 years, respectively. Mean weight change at 12 months was -7.5%. Member NDQS increased from baseline to T1 (11.5 ± 3.2 vs 14.1 ± 2.4, p 0.05). At T2, 40.7% of members reported encouraging others in their household to become more active, and this proportion remained consistent at T4 (40.5%, p > 0.05).
Conclusion
Although the low response rate across all three surveys is a limitation, the findings suggest that Slimming World’s behaviour change programme is effective in supporting adults (mainly females) living with obesity to make health-related behaviour changes. Members achieved clinically significant weight loss and improvements in diet quality, physical activity, and mental well-being compared with the reference group. These changes were maintained at 12 months, with an additional positive influence reported on family members’ lifestyle habits
Pharmaceutical Supply Chain Management Challenges in Developing Countries: A Systematic Literature Review
Get PDFopen access articleCounterfeit medicines endanger public health, undermine economic stability, and present a critical global challenge, especially in developing countries such as Jordan. Addressing this issue necessitates the modernization of pharmaceutical supply chain management (SCM) through the adoption of advanced digital technologies, including Blockchain, the Internet of Things (IoT), and Radio Frequency Identification (RFID). This study develops a conceptual model grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), augmented by the construct of technology trust and contextualized through institutional theory. Employing a qualitative methodology; semi-structured interviews with key stakeholders of the Jordanian pharmaceutical supply chain, the study reports the critical role of technology trust in strengthening stakeholders' behavioral intentions toward technology adoption. Moreover, it finds that institutional pressures significantly shape adoption behaviors. Thus, by integrating theoretical frameworks and offering practical insights, the study contributes to the literature addressing the counterfeit medicine crisis and provides a comprehensive framework to facilitate technology-driven transformation within Jordan's pharmaceutical supply chain
A fitness spatially informed evolutionary algorithm for deceptive multi-modal multi-objective optimization
Get PDFThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Evolutionary Algorithms (EAs) are effective for solving Multi-Modal Multi-Objective Optimization Problems which optimal solutions subsets distributed regularly in the decision space. Existing EAs, however, are hard to achieve good diversity in the decision space due to their lack of thinking-in-space when solving deceptive multi-modal multi-objective problems, and one manifestation of the deceptiveness is that global optima are composed of unconnected small optimal solution subsets that randomly distributed in the decision space with many local optima subsets mixed among. In this study, for enabling EAs to continuously search without trapping, a solving pathway is proposed that identifies information-rich historical data and constructs structured spatial representation based on high-quality evolutionary data. Furthermore, a moderate region-oriented solution set update methodology is proposed for the detection of ultra-small optimal modalities, and two novel environmental selection and archive update mechanisms are designed. A Memory-aiding Search Multi-Modal Multi-Objective Optimization EA, i.e., MS-MMOEA, is instantiated. The experimental results on 27 deceptive instances and a real-world problem demonstrate MS-MMOEA’s competitiveness over the state-of-the-art
Multi-agent cooperation-based bi-criteria evolutionary many-objective optimization
No full textThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Many-objective evolutionary algorithms (MaOEAs) excel in solving many-objective optimization problems (MaOPs), which are mainly classified into two frameworks: the Pareto domination and the non-Pareto domination. The Pareto criterion (PC) obtains a well-converged solution set in multi-objective spaces through the Pareto dominance relationship between solutions. However, insufficient environmental selection pressure in many-objective spaces leads to slow convergence. The non-Pareto criterion (NPC) enhances the selection pressure by evaluating the solution set with a set of sortable scalar values. However, it is difficult to ensure the Pareto-optimal consistency of convergence and distribution when facing highly irregular Pareto fronts (PFs). Therefore, combining the two sets of criteria can satisfy the demand for uniform distribution while bringing significant selection pressure. A multi-agent cooperative strategy is proposed in this study to realize the combination of the two criteria. This strategy controls the evolutionary direction of two populations separately by deploying two agents, and promotes cooperative evolution between these populations through the exchange and flow of large amounts of information. In order to better realize the cooperative effect, we adopt the multi-agent reinforcement learning (MARL) strategy to accurately regulate the variation operator and parameter configurations of the bi-population. In addition, the effectiveness of the proposed method is validated on 74 test problems (DTLZ, WFG, and UF) and 3 real-world problems. The results show that the proposed algorithm is more competitive than 6 state-of-the-art algorithms
Evaluating Green Marketing Strategies: Role of Sustainability Indicators
No full textThe increasing rise of environmental problems and the necessity of sustainable development have made firms adopt green marketing practices. These strategies aim to promote products and services that are eco-friendly, lower carbon footprints, and improve corporate social responsibility. This chapter discusses the evaluation of such strategies with a central focus on the role of sustainability indicators in determining their effectiveness. This chapter examines how sustainability indicators can be used by firms to evaluate green marketing strategies effectively. Through the use of indicators of sustainability, firms can make well-informed decisions, improve their environmental performance, and gain consumer trust. In turn, this contributes to the academic discourse on green marketing and offers practical insights for practitioners aiming to achieve sustainable growth. This chapter contributes to green marketing theory by offering a structured approach to evaluating sustainability initiatives. The methodology used in the thematic literature review was assessed with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Statement. The thematic literature review yielded several significant findings regarding the effectiveness of green marketing strategies and the role of sustainability indicators
Immersing and Perceiving in Tourism Scenarios: The Interaction Mechanism between Digital Technologies and Tourists
No full textThis study explores the impact of digital technologies on immersive tourism experiences, focusing on the interaction mechanisms between these technologies and tourists. With the rise of VR, AR, mobile apps, and wearables, digital tools have become pivotal in enhancing tourist engagement and satisfaction. The main contribution of this research is the development of a systematic framework for constructing immersive tourism experiences. This framework involves collecting and analysing tourism resource data, selecting appropriate technologies, and generating interaction mechanisms. Additionally, a comprehensive case study categorises tourism resources into cognitive, emotional, aesthetic, and intelligent interactions, and validates the framework through statistical analysis. The findings reveal that these interactions significantly contribute to creating high-quality immersive experiences. Recommendations for integrating digital technologies into tourism are provided to enrich tourist experiences and foster deeper connections with destinations. This research offers valuable insights into the effective utilisation of digital tools in tourism, paving the way for innovative and captivating tourist engagements
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