The research, led by Soumya Liz Jacob and Malathi G. Nayak of the Manipal College of Nursing at Manipal Academy of Higher Education, with colleagues from institutions including the University of Edinburgh, set out to close a gap that has long been acknowledged but rarely addressed with methodological precision. Existing palliative care competency frameworks, most of them developed in high-income countries, assume healthcare systems, training pathways and scopes of nursing practice that do not translate cleanly to the Indian context. In India, family members frequently carry much of the hands-on burden of caring for the dying, specialist palliative services are concentrated in a handful of regions, and nurses themselves may receive little formal education in symptom management, communication around death, or the ethical dilemmas that surround end-of-life decisions. A framework imported wholesale from elsewhere risks being irrelevant at best and misleading at worst.

To build something genuinely grounded in Indian realities, the team began with a systematic synthesis of the published literature and paired it with a qualitative inquiry involving in-depth interviews and focus group discussions. From this dual foundation they drafted an initial pool of competency statements, each one intended to describe an observable skill, attitude or body of knowledge that a nurse providing palliative care in India should possess. The draft was then subjected to a modified electronic Delphi process, a structured technique for achieving group consensus that has become a mainstay of health-professional standards development. In a Delphi study, experts rate statements independently across successive rounds, receiving anonymised feedback about the group’s responses between rounds, which allows views to converge without the pressure dynamics of a face-to-face meeting.

The modified e-Delphi used here ran across three rounds and involved palliative care experts from across India, producing a geographically diverse evidence base that the researchers argue is essential for a country as heterogeneous as India. Consensus was defined in advance with statistical precision: a statement would be considered endorsed only if the median expert rating reached at least four on the rating scale, the interquartile range was no greater than one, and at least seventy percent of panellists agreed. Setting these thresholds before data collection protects the process from the criticism, common in weaker Delphi studies, that consensus criteria were chosen after the fact to fit the results. The researchers also reported their work according to the Conducting and Reporting of Delphi Studies guidance, and the trial was registered with the Clinical Trials Registry of India in July 2023, an unusual but welcome step for a study of this type.

The statistical machinery behind the study deserves attention because it illustrates how modern competency validation works. Beyond the descriptive consensus thresholds, the team examined changes in expert ratings between Rounds 2 and 3 for directly comparable statements using Wilcoxon signed-rank tests, a non-parametric method suited to paired ordinal data. Because they were testing many statements simultaneously, they applied the Benjamini-Hochberg correction to control the false discovery rate, preventing spurious signals of change from accumulating across dozens of comparisons. This combination of techniques reflects a broader trend in nursing science: the recognition that frameworks meant to guide education and policy must be built on transparent, reproducible quantitative foundations rather than on the intuitions of a small committee.

The results were striking in their consistency. Health professionals from fifteen states participated, and levels of agreement were high across all rounds. In the first round, most items were retained, while expert feedback prompted the researchers to merge overlapping statements, modify wording, delete redundancies and add missing content. By the final round, seventy-six of the seventy-seven statements met all the predefined consensus criteria. The single remaining statement fell short of the full statistical thresholds but was retained on the strength of its median rating and the qualitative feedback from the preceding round that had originally led to its inclusion, a decision the authors report transparently rather than quietly burying.

The final framework comprises nine domains and seventy-seven competency statements, and its architecture reads like a map of what dignified dying actually requires. The domains cover the foundations of palliative care; therapeutic communication; physical comfort and symptom management; multidisciplinary collaboration; psychosocial, emotional and spiritual support; end-of-life care; family and caregiver support; ethical and legal aspects of care; and, notably, nurse self-care and professional development. The inclusion of that last domain is significant. Compassion fatigue and burnout are well documented among clinicians who care for the dying, and a framework that treats the nurse’s own psychological sustainability as a competency, rather than as a private problem, represents a maturing of the field.

Several of the domains speak directly to the Indian context that motivated the study. Family and caregiver support, for instance, acknowledges that in India the family is typically the primary unit of care, and that a nurse’s job includes coaching exhausted relatives in symptom management, preparing them for the trajectory of decline, and supporting them through bereavement. The spiritual support domain reflects the profound religious and cultural diversity of the country, where rituals surrounding death vary enormously across communities and where a competent nurse must navigate them with sensitivity rather than a single template. The ethical and legal domain addresses questions such as advance directives and end-of-life decision-making that Indian law and clinical practice have only recently begun to confront.

The researchers are careful about what the framework can and cannot do. Its stated purpose is to support nursing education, clinical practice and policy, providing curriculum designers with a validated blueprint for training programmes, giving hospital administrators a benchmark for evaluating and developing staff, and offering policymakers a technical foundation for initiatives under the National Programme for Palliative Care. But the authors explicitly note that further work and commitments are needed to implement the framework in real-world practice. Validation of content is a beginning, not an endpoint; the framework must now be tested in educational settings, translated into assessment tools, and adopted by the institutions that train and employ India’s nurses. The study received no external funding, with open access support provided by Manipal Academy of Higher Education, and the authors declare no competing interests.

The significance of the work extends beyond India’s borders. The World Health Organization has repeatedly identified palliative care as an ethical imperative and a component of universal health coverage, enshrined in Sustainable Development Goal 3, yet the majority of the global need sits in low- and middle-income countries where specialist services are scarce and generalist nurses carry the load. A validated, context-specific competency framework offers those countries a template for strengthening the largest available workforce. For India, where millions of people each year need palliative care and receive none, the seventy-seven statements validated by experts from fifteen states are more than an academic exercise. They are a concrete statement of what good care at the end of life looks like, and a demand that the systems training nurses be held to it.

Subject of Research: Development and content validation of a palliative care nursing competency framework for India using a modified Delphi method

Article Title: Nursing competencies in palliative care for India: content validation using a modified Delphi approach

Article References: Jacob, S. L., Nayak, M. G., George, L. S., Noronha, J. A., Macaden, L., & R., V. (2026). Nursing competencies in palliative care for India: content validation using a modified Delphi approach. BMC Nursing. https://doi.org/10.1186/s12912-026-05416-y

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05416-y

Keywords: palliative care, nursing competencies, Delphi method, India, nursing education, end-of-life care, consensus framework, symptom management, nurse self-care, health policy, Nursing, competencies

The framework operates within a Pythagorean fuzzy multi-scale information system, a setting designed for situations in which different decision makers grade their assessments at different levels of granularity. One expert might rate a transportation option simply as poor, fair, or excellent, while another provides a finely tuned linguistic scale with seven or nine gradations. The researchers first deploy a recursive scale-expansion mechanism that converts these heterogeneous Pythagorean fuzzy evaluations into representations at different levels of granularity. Pythagorean fuzzy sets extend ordinary fuzzy logic by assigning each element both a membership degree and a non-membership degree whose squares must sum to no more than one, giving evaluators more room to express hesitation. The recursive mechanism ensures that opinions expressed at coarse scales can be consistently compared and merged with those expressed at fine scales without discarding information in the process.

What distinguishes the new framework from earlier consensus models is its refusal to treat decision makers as identical rational agents. Decades of behavioral economics research, most famously the work of Daniel Kahneman and Amos Tversky, have shown that real people distort probabilities, fear losses more than they value equivalent gains, and weigh unlikely events in systematically biased ways. The team captures this heterogeneity through a hybrid behavioral model that combines cumulative prospect theory with rank-dependent utility. Cumulative prospect theory describes how individuals evaluate gains and losses relative to a reference point, weighting outcomes by a distorted probability function, while rank-dependent utility transforms cumulative probabilities in a rank-ordered fashion. Merging the two yields five psychological parameters for each decision maker: sensitivity to gains, sensitivity to losses, a loss-aversion coefficient, and two curvature parameters describing probability distortion in the gain and loss domains.

Estimating these parameters is far from trivial, and the paper devotes a detailed appendix to the problem. The authors designed a structured questionnaire of nineteen binary choices, each pitting a sure outcome against a probabilistic lottery, following the standard elicitation paradigm in behavioral decision theory. Participants switch preferences at points that reveal their certainty equivalents for various lotteries, and those switching points allow the researchers to back out each parameter. Gain sensitivity, for example, is estimated from the ratio of the probability weighting at even odds to the logarithm of the normalized certainty equivalent, while the loss-aversion coefficient follows from the indifference gain and loss identified in a mixed gamble. Probability-weighting curvatures are fitted by least squares across probability levels ranging from one percent to eighty percent. The team notes that this choice-based approach reduces the cognitive burden on respondents and improves the reliability of the resulting estimates compared with direct questioning.

Once every decision maker carries a psychological profile vector, the framework clusters participants hierarchically according to the similarity of their estimated parameters. This step recognizes that a risk-seeking optimist and a loss-averse pessimist will interpret the same fuzzy evaluation in profoundly different ways, and that consensus mechanics should account for those differences rather than averaging them away. The clustering partitions the large group into subgroups of psychologically like-minded members, each of which can then be managed with its own consensus strategy. Within each subgroup, social network analysis takes over: by mapping who trusts, consults, or influences whom, the method computes degree-centrality weights that identify the most connected and persuasive individuals. Feedback and opinion adjustment are targeted at those influential members, amplifying the effect of each intervention without forcing every participant to revise their views repeatedly.

The second layer of the framework coordinates the subgroups themselves, and this is where machine learning enters the picture. A regularized graph attention network learns adaptive weights for the different clusters, attending more strongly to subgroups whose current positions matter most for closing the consensus gap. Graph attention networks compute weights dynamically from the structure and content of the network rather than fixing them in advance, which makes them well suited to the shifting landscape of a consensus process as opinions evolve across iterations. Crucially, the authors add a regularization term that penalizes excessive weight concentration, preventing the model from pouring all its attention into one dominant cluster and neglecting the tail of smaller groups whose agreement is still needed. The result is an inter-group negotiation that adapts its weighting scheme as the discussion progresses while remaining numerically stable.

To demonstrate the machinery in action, the researchers applied the framework to a real-world class of problem: selecting a low-carbon urban transportation scheme. Such decisions are ideal testbeds because they involve large panels of stakeholders, from traffic engineers to environmental planners, who naturally differ in both the precision of their assessments and their tolerance for risk about future costs and emissions. When the authors benchmarked their method against five representative group decision-making approaches, the double-layer framework reached the prescribed consensus threshold in fewer inter-group iterations and with lower total adjustment cost, meaning that experts had to modify their opinions less aggressively to reach collective agreement. The method also preserved more of the original evaluation information, an important consideration since forcing coarse or over-smoothed judgments erodes the very expertise that large panels are convened to harvest.

The team went beyond a single demonstration, subjecting the framework to both ablation studies and sensitivity analyses. In the ablation experiments, removing or disabling individual components—such as the behavioral clustering, the social-network-based influence weighting, or the regularization of the attention network—degraded performance, confirming that each element contributes measurably to the overall result. The sensitivity analyses varied key parameters across plausible ranges and found that the outcomes remained stable, suggesting that the method is not a fragile artifact of carefully tuned settings but a robust procedure that practitioners could realistically deploy. That robustness matters, because consensus models that only work under idealized conditions rarely survive contact with the noisy, deadline-driven environments of municipal planning and corporate strategy.

The implications extend well beyond transportation policy. Any setting in which many stakeholders with vague, multi-granularity information and divergent risk attitudes must converge—health technology assessment, disaster response planning, infrastructure investment, environmental regulation—could benefit from a consensus mechanism that respects both the uncertainty of the data and the psychology of the people. By uniting Pythagorean fuzzy mathematics, behavioral economics, social network analysis, and graph neural networks in a single coherent architecture, the Shanxi University team has sketched a template for what next-generation group decision support might look like: models that do not pretend humans are perfectly rational, information is perfectly comparable, or influence is perfectly flat, but instead build those imperfections into the mathematics of agreement itself. The work was supported by the Humanities and Social Sciences Research Project of the Ministry of Education of China, and the article is available open access.

Subject of Research: Group decision-making with multi-scale fuzzy information and behavioral preferences

Article Title: A double-layer consensus framework for group decision-making with multi-scale fuzzy information and behavioral preferences

Article References: Chai, M., Fan, J., Lu, J., Wu, M., Cheng, R., & Chen, R. (2026). A double-layer consensus framework for group decision-making with multi-scale fuzzy information and behavioral preferences. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-026-02492-0

Image Credits: AI Generated

DOI: 10.1007/s40747-026-02492-0

Keywords: group decision-making, Pythagorean fuzzy sets, consensus framework, cumulative prospect theory, rank-dependent utility, social network analysis, graph attention network, behavioral preferences, multi-scale information system, loss aversion, machine learning, low-carbon transportation