Cancer survivorship frequently involves confrontation with mortality, yet qualitative findings on meaning-making and existential experiences remain fragmented. This PRISMA 2020-guided systematic review synthesized qualitative evidence on meaning-making among adult cancer survivors across Scopus, Web of Science, MEDLINE, and the Psychology & Behavioral Science Collection (final search: 25 November 2025). Twenty-eight qualitative studies were included and synthesized using thematic synthesis, with confidence assessed via GRADE-CERQual. Five themes were identified: confrontation with mortality and existential shock; existential suffering and biographical disruption; meaning reconstruction and reorientation; transcendence, spirituality, and faith-based meaning; and psychological adaptation, resilience, and growth. Across diverse cultural contexts, cancer survivorship consistently involved mortality awareness and existential destabilization, followed by efforts to restore coherence and purpose. Spirituality, self-transcendence, and resilience emerged as prominent coping pathways. These findings support meaning-centered and existentially informed psychosocial care with culturally sensitive spiritual resources.
Engaging in volunteer work could be associated with posttraumatic growth (PTG). Investigating the associations among motivations to volunteer, PTG, and grief can help us understand how people cope with death. Volunteer functions theory posits six functions of volunteer motivations: protective, values, career, social, understanding, and enhancement. We studied the associations that each function has with grief intensity, PTG, and current volunteer status. Participants (N = 187) with volunteer experience who experienced death completed an online study. Hierarchical regression analyses revealed that grief intensity was associated with the protective function. PTG was associated with values and enhancement functions. Current volunteer status was associated with the career function. The findings suggest that grief intensity and PTG are related to some but not all motivations to volunteer, highlighting how the psychological experience of bereavement relates to motivations to engage in prosocial behavior.
As emotionally responsive AI systems are increasingly used for companionship, clinicians are encountering forms of distress that do not fit established models of bereavement. When these systems are altered, restricted, or lost, some users experience reactions resembling attachment disruption and relational loss. Here, AI companions are conversational systems supporting ongoing relational bonds; we address grief for their disruption, not AI simulations of deceased people. This paper argues that AI companion bereavement is best understood as a technologically mediated hybrid loss at the intersection of ambiguous loss and disenfranchised grief. We introduce the concept of dual illegibility to describe uncertainty both about what has been lost and whether that loss is socially recognised as legitimate. Drawing on grief theories and emerging research on human-AI relationships, we outline a preliminary clinical response model and consider implications for practice, ethics, and research in an evolving landscape of technologically mediated attachment.
India has 46.4 million widows, the highest number globally, with thousands migrating to Vrindavan, the "City of Widows," where they experience systematic social exclusion and marginalization (Loomba Foundation, 2015; Pathak & Tripathi, 2016). This study examines the socio-psychological, cultural, and economic problems faced by widows in Vrindavan through the lens of social death theory (Goffman, 1963; Kastenbaum, 1977). A convergent parallel mixed-methods design (Creswell & Plano Clark, 2017) employed structured interviews (N = 258) and qualitative case narratives. Participants were selected via stratified random sampling from government-run ashrams (n = 101), NGO-run ashrams (n = 56), and rented accommodations (n = 101). Quantitative data were analyzed using chi-square tests, and qualitative data were analyzed using thematic analysis (Braun & Clarke, 2006). Integration occurred at the interpretation stage using a weaving approach (Fetters et al., 2013). All respondents reported experiencing stigma (100%); 93.8% received no family visitors; 98.8% reported self-reported psychological symptoms; and 71.7% had no pension. Economic independence was significantly associated with spending capacity (χ2 = 126.502, df = 3, p < .001). Education was associated with lower levels of self-reported depressive symptoms (χ2 = 19.019, df = 20, p = .021). Qualitative thematic analysis revealed four key themes: (1) The Family as Site of Danger, (2) Internalized Stigma and Self-Exclusion, (3) Institutional Care and Its Limits, and (4) Resilience amid Social Exclusion. Widows in Vrindavan experience systematic social exclusion before biological death through four mechanisms: ritual degradation, spatial exclusion, affective abandonment, and economic destitution. The study extends social death theory by demonstrating that social erasure is a cumulative process rather than a binary state, and that cultural stigma operates independently of economic status. Policy interventions for pension coverage, mental health support, legal awareness, and dignified care are urgently needed.
Many bereaved persons who experience persistent yearning, a transformed sense of identity, and ongoing connection to the deceased, all at once, find themselves either pathologized by existing diagnostic frameworks or left without a conceptual language for what they are living. This article identifies a common root for the major debates surrounding the delimitation of Prolonged Grief Disorder (PGD): the absence of a framework that treats the co-occurrence of loss and continuing presence as a constitutive structure of grief. Drawing on the concept of doluance, defined as the constitutive simultaneity of rupture and transformed presence in bereavement, the article evaluates the heuristic reach of this framework beyond its context of origin (Canada, COVID-19 pandemic). The analysis is developed through the close examination of four emblematic items from current PGD assessment instruments and outlines an empirical agenda whose initial hypotheses can be tested with existing datasets.
This paper investigates technology adoption for the decarbonization of supply chains under a cap-and-trade policy. A regulator sets a declining emissions cap over time and allocates a fixed budget across facilities, which then choose technology upgrades, production/procurement plans, and carbon trading to satisfy demand and comply with caps at minimum cost. We formulate the problem as a bilevel mixed-integer program combining plant-period caps, discrete monotone technology ladders, and facility budget dynamics with earmarked trading revenues. We propose three solution methods and compare them on extensive synthetic test beds. A steel supply chain case shows how transitional technologies and trading can accelerate the adoption of hydrogen-based routes and achieve the targeted emission abatement path. The results highlight when limited public funds are most effectively deployed and how trading complements intertemporal adoption.
Standard Data Envelopment Analysis radial models such as CCR and BCC assume that inputs are substituted for each other along the efficiency frontier. However, this assumption is rarely verified empirically, creating the risk of specification bias if factors are not substituted. This paper introduces a nonparametric testing protocol to determine whether inputs have, in fact, been substituted for each other along the efficiency frontier. It employs Local Weighted Quantile Regression to estimate the partial derivatives of the input frontier directly from the data, classifying input pairs as substitutes or non-substitutes based on the sign and statistical significance of the estimated frontier slopes. The methodology is illustrated using Monte Carlo simulations of known substitute and non-substitute technologies. When applied to 2534 U.S. commercial banks, the protocol finds no evidence of substitution for any input pair: 13 of 15 specifications classify the inputs as non-substitutes that increase and decrease together, and the remaining two yield positive but imprecisely estimated slopes. The paper then shows that applying the standard CCR model to the bank data results in substantial biases in bank efficiency scores. Finally, the paper identifies models that have been used to account for fixed proportion technologies.
Grocery retailers often face the decision of whether to allow freshness-based demand substitution by offering the same product with differing shelf lives. Offering both "new" and ''old" units simultaneously enhances market penetration but also cannibalizes demand for new units, as old units are typically sold at a discount. This paper develops an analytical framework that extends the classical Economic Order Quantity (EOQ) model to explicitly capture intra-cycle competition between old and new units of a perishable product with a fixed shelf life (e.g., a carton of milk). We derive optimal policies under single-batch (no competition) and double-batch (competition within each cycle) replenishment strategies and characterize the conditions under which each policy is profit-maximizing. Accordingly, there exists a shelf-life threshold below which the single-batch policy is optimal, whereas a longer shelf life may justify adopting the double-batch policy. Interestingly, under the double-batch policy, both the optimal cycle length and order quantity are smaller, implying that a longer shelf life does not necessarily increase inventory levels. Partial competition, where intra-cycle competition is shorter than the cycle length, is rarely optimal and yields marginal profit improvement. We further extend the model by proposing a pricing-EOQ formulation, where markdown depth is endogenously determined, to assess the robustness of the findings and highlight the role of markdowns. The central findings remain true and the firm makes a deep markdown only when market expansion is limited. Our results identify when freshness-based competition should be avoided or strategically embraced.
In this work, we propose a consistency-based and interpretable linear-time algorithm named Orthogonal Regression BWM (OR-BWM) for solving the Best-Worst Method (BWM). The existing techniques model the BWM as an optimization problem over weights. However, we model it as a linear orthogonal regression problem in a two-dimensional space defined by the best and the worst criteria. We represent the criteria weights as points in this plane. The decision-maker (DM) provides the best-to-others and the others-to-worst preference vectors, which are elicited independently. However, these vectors are connected by the underlying consistency constraint inherent to the BWM framework. We find the optimal best-to-worst ratio, as the slope of an orthogonal regression line. We compute the weights of the criteria by projecting the DM's input onto the constructed regression line. The resulting algorithm runs in O(n) time, where n is the number of criteria. We evaluate OR-BWM against state-of-the-art methods, including the original Min-Max nonlinear formulation, its linear programming variant, the Euclidean distance-based approach, and the Logarithmic Least Squares Method (LLSM). Across extensive experiments, OR-BWM performs the best in terms of execution time, demonstrates the strongest robustness and stability under input perturbations (noisy inputs), reflected by minimal L2 weight deviation and best rank preservation, ranks among the top two in terms of consistency, and achieves above-average performance in minimum-violation and reconstruction error metrics.
Telemedicine has emerged as a strategic complement to traditional in-person care, offering enhanced continuity and effectiveness through blended treatment that integrates online and offline visits to support continuous care. We examine two distinct delivery modes for blended treatment, namely the single-hospital and cross-hospital modes, in an asymmetric competition setting involving atelemedicine-capable hospital and a traditional hospital. Specifically, in the single-hospital mode, the telemedicine-capable hospital delivers both online and offline care components internally. In the cross-hospital mode, the telemedicine-capable hospital provides the online component, while either hospital can deliver the offline component. Utilizing a game-theoretic framework, we examine the strategic adoption of these two modes by the telemedicine-capable hospital and analyze the resulting competitive and operational implications. Surprisingly, we find that a telemedicinecapable hospital may suffer losses from launching blended treatment, as the gains from market expansion are outweighed by intensified competition and internal cannibalization, resulting in lower profits. Furthermore, even when blended treatment is profitable, the telemedicine-capable hospital may still prefer the single-hospital mode over the cross-hospital mode under certain conditions, even at the expense of its rival's profits, while consistently improving both patient and social welfare. By contrast, adopting the cross-hospital mode can yield a triple-win outcome for the telemedicine-capable hospital, its rival, and patients, and also enhance social welfare. This study advances theoretical understanding of healthcare system dynamics and provides strategic recommendations for healthcare organizations navigating digital transformation.
Individuals frequently make suboptimal judgments in inventory and supply chain decisions. In the newsvendor problem, they often exhibit a "pull-to-center" bias, placing orders below the optimal level yet above the average demand for high-margin items, and vice versa for low-margin items. This paper examines whether impulsivityrelated personality traits help explain this pull-to-center effect and whether the bias can be reduced. Across two studies, less impulsive individuals, those with higher premeditation scores, tend to order closer to the optimal quantity. Study 1 shows that this advantage is more pronounced under indirect decision support but diminishes when participants receive direct decision support. Study 2 replicates the association between higher premeditation and lower order bias in a classical setting with normal demand and no decision support, confirming that the effect generalizes beyond the conditions of Study 1. An episodic future thinking (EFT) intervention in Study 2 attenuates the premeditation advantage, closing the gap under the low-margin condition while only partially narrowing it under the high-margin condition. The two studies show that premeditation shapes decision making under uncertainty, and that this effect can be attenuated by adjusting the form of decision support or by a brief cognitive intervention. These findings can inform how training and AI-assisted decision support are designed for decisions under uncertainty.
Measuring and improving public sector efficiency is key to delivering fair and effective services. As part of the public sector, justice courts, beyond handling legal matters, should pursue the efficient allocation of public resources. Therefore, their economic efficiency should be regularly assessed. However, studies evaluating the performance of justice courts rarely focus on the economic dimension of efficiency at the micro level. To fill this gap, the contribution of this study is twofold. Firstly, from the methodological point of view, relying on data envelopment analysis, we propose a new hybrid framework for measuring cost efficiency that is capable of handling exogenously fixed and non-fixed prices and allowing the latter to be endogenously optimized. Other advantages of the proposed framework include estimating realistic targets for non-fixed prices and utilized resources, exploring reallocation opportunities for resources, and identifying cost–demand relationships. Secondly, from the empirical perspective, we show the applicability of this new framework to Polish district courts over the period 2017–2021. The results reveal that only 10% of the courts are cost efficient. Interestingly, on average, the model suggests an increase in the number of all employees and in non-fixed salaries, as well as a reduction in the volume of capital. The results also suggest that the reallocation of resources can lead to significantly fewer pending cases or can eliminate them entirely. A further reduction in the number of pending cases can be potentially achieved by redistributing all registered cases under a centralized management system.
This paper introduces a two-stage stochastic programming model featuring multi-objective recourse functions to address the economic, environmental, and social dimensions of sustainability in steel supply chain networks. The model integrates strategic decisions (facility location, capacity acquisition, and technology selection) with tactical material flow coordination. The dynamics of steel industry-specific characteristics are captured through multi-scale time periods, technology compatibility requirements, and sustainability metrics based on location-technology-flow configurations. Furthermore, the model incorporates export taxes and import tariffs to optimise material flows between nationwide and global networks, enabling comprehensive analysis of how these policy tools influence strategic and tactical decisions whilst impacting sustainability objectives. A solution approach is developed using the ε-constraint and Sample Average Approximation methods to address demand uncertainty and the trade-offs among multiple recourse objectives. Novel algorithms are introduced to determine the upper and lower bounds of the constrained objective functions and to form the loops of the ε-constraint method. The model's application to a real-world case study demonstrates its effectiveness in achieving balanced material flows across the steel SCN after four strategic periods. Through sustainability assessment and analysis of various tax and tariff policies, we derive eight policy insights regarding their impacts on strategic and tactical decisions. The results confirm the model's capability to generate robust solutions that effectively balance network capacity against demand patterns while achieving sustainability objectives.
