The global population is ageing rapidly, emphasising the need to understand the decision-making processes of older adults regarding potential care transitions. Gerontological research has focused on healthcare decisions, with less information on living situation choices of older adults. This review explored older adults’ experiences with their involvement in decision-making processes related to transitioning into care facilities in the United Kingdom. From a systematic search of articles, nine were reviewed using thematic narrative synthesis. Four themes with nine subthemes were identified: Involvement in decision-making (Exclusion of older adults, Usefulness of involvement), The necessity of moving (Triggers for moves, The role of family), Timely planning (Helpfulness of planning, Planning avoidance), and Factors for choosing a care home (Non-quality factors, Quality factors, Continuity of life). These themes highlighted the issue of inadequate involvement of older adults in decision-making, often resulting in negative consequences like regret and difficulty settling into new care settings. The necessity of moving arose from sudden events or increased support needs. Some older adults acknowledged the necessity due to declining health or to spare family burden, while relatives grappled emotionally, postponing the choice. Timely planning was found to be beneficial practically and emotionally, facilitating smoother transitions. However, participants would rarely plan and discuss such matters early. Older adults focused on personal experiences and trusted sources rather than publicly available information when considering Factors for choosing a care home. The findings show the need for greater inclusion of older adults in decisions related to their care and the importance of early planning and providing preferred types and formats of information to aid decisions. Future research should focus on a better understanding of older adults’ preferences for successful involvement in care decisions, with support and guidance for others involved in the decisions.

Following the large-scale Russian invasion in February 2022, policymakers and humanitarian actors urgently sought to anticipate displacement flows within Ukraine. However, existing internal displacement data systems had not been adapted to contexts as dynamic as a full-fledged war marked by uneven trigger events. A year and a half later, policymakers and practitioners continue to seek forecasts, needing to anticipate how many internally displaced persons (IDPs) can be expected to return to their areas of origin and how many will choose to stay and seek a durable solution in their place of displacement. This article presents a case study of an anticipatory approach deployed by the International Organization for Migration (IOM) Mission in Ukraine since March 2022, delivering nationwide displacement figures less than 3 weeks following the invasion alongside near real-time data on mobility intentions as well as key data anticipating the timing, direction, and volume of future flows and needs related to IDP return and (re)integration. The authors review pre-existing mobility forecasting approaches, then discuss practical experiences with mobility prediction applications in the Ukraine response using the Ukraine General Population Survey (GPS), including in program and policy design related to facilitating durable solutions to displacement. The authors focus on the usability and ethics of the approach, already considered for replication in other displacement contexts.

Scholars have identified several temporal challenges in foreign policymaking, such as variable time horizons and maintaining commitment or resolve over time. While the behavioural turn has emphasised leaders and their subjective perceptions, research often relies on rationalist conceptions of objective and linear time and struggles to assess leaders’ subjective perceptions of it. This paper theorises time as an intrinsic aspect of narrative reasoning in foreign policy, introducing a ‘temporal definition of the situation’ (TDoS) framework to capture leaders’ situation-specific subjective time perceptions. I then operationalise the TDoS framework’s key temporal features and show how it can be empirically examined. The value of the TDoS is illustrated by assessing the temporal perceptions of Bush and Obama regarding Iran’s nuclear programme, showing how their distinct definitions of the foreign policy situation shaped their subjective time perceptions and their corresponding responses. I conclude by discussing how this advancement can enhance behavioural research, provide insights into the ‘why now?’ questions surrounding leaders’ actions, and challenge existing understandings of time’s impact on foreign policymaking.

Aviation employees operate in a dynamic, complex safety-critical system that is filled with uncertainty, requiring quick and correct expert decision-making. The purpose of this study is to investigate the decision-making indicators among aviation employees. Fifty-five technical engineers and air traffic controllers participated in this study by completing the Cambridge Gambling Task (CGT) at one of Iran’s airports. The CGT provides one of the most reliable and widely used decision-making assessment tasks and related indicators, including decision-making quality, risk-taking, delay aversion, deliberation time, risk adjustment and overall bet ratio. Higher risk adjustment, less deliberation time, and a lower delay aversion index resulted in better decision-making quality. Higher risk-taking does not necessarily mean lower self-control. No significant differences were observed between the studied groups, including between air traffic controllers (both Ground and Tower vs. RADAR and Approach) and between air traffic controllers and technical engineers in the CGT performance. The decision-making quality increased with age and work experience, which has important implications for training and selection processes.

Translating emerging health technologies towards adoption and patient benefit requires timely and effective research and development decisions. Early health technology assessment has a key role to play in supporting these decisions. A new consensus definition of early health technology assessment is a welcome contribution to help bring these activities toward wider use in the field. In parallel, the opportunities to perform early health technology assessment activities are increasing as new types of health technologies begin to enter healthcare systems globally. A greater focus on transparency of reporting, improving awareness around how early health technology assessment can impact decision-making, increased resourcing for these activities, expanding training for analysts, and encouraging collaboration between individuals across healthcare systems will be vital to strengthen the uptake of early health technology assessment from this point forward.

Centralisation of powers typically occurs in times of crisis. The paper investigates and compares the intergovernmental relations (IGRs) in the Italian decentralised systems during the economic and financial crisis (2008–2013) and the COVID-19 pandemic (2020–2022). During both these two phases, Italy experienced a transition from a political government to a technical one. During the economic and financial crisis, Silvio Berlusconi's government (2008–2011) was succeeded by a technical one led by Mario Monti (2011–2013); similarly, during the pandemic, Giuseppe Conte's government (2020–2021) was followed by a technical one led by Mario Draghi (2021–2022). The hypothesis is that the presence of ‘political’ governments still guarantees a certain degree of cooperation with lower levels of government (i.e. regional and local administrations), while ‘technical’ governments further exacerbate the centralisation of powers. The paper analyses the legislative activities of the central government and the documents of the Italian ‘conference system’ during the two periods of analysis. According to our hypothesis, the findings show a greater centralisation of power under the technical government during the pandemic, but not during the economic crisis. This outcome suggests that the policy domain may serve as a main intervening factor over the degree of centralization of the IGRs during periods of crisis.

The treatment recommendation based on a network meta-analysis (NMA) is usually the single treatment with the highest expected value (EV) on an evaluative function. We explore approaches that recommend multiple treatments and that penalise uncertainty, making them suitable for risk-averse decision-makers. We introduce loss-adjusted EV (LaEV) and compare it to GRADE and three probability-based rankings. We define properties of a valid ranking under uncertainty and other desirable properties of ranking systems. A two-stage process is proposed: the first identifies treatments superior to the reference treatment; the second identifies those that are also within a minimal clinically important difference (MCID) of the best treatment. Decision rules and ranking systems are compared on stylised examples and 10 NMAs used in NICE (National Institute of Health and Care Excellence) guidelines. Only LaEV reliably delivers valid rankings under uncertainty and has all the desirable properties. In 10 NMAs comparing between 5 and 41 treatments, an EV decision maker would recommend 4–14 treatments, and LaEV 0–3 (median 2) fewer. GRADE rules give rise to anomalies, and, like the probability-based rankings, the number of treatments recommended depends on arbitrary probability cutoffs. Among treatments that are superior to the reference, GRADE privileges the more uncertain ones, and in 3/10 cases, GRADE failed to recommend the treatment with the highest EV and LaEV. A two-stage approach based on MCID ensures that EV- and LaEV-based rules recommend a clinically appropriate number of treatments. For a risk-averse decision maker, LaEV is conservative, simple to implement, and has an independent theoretical foundation.

Real-world evidence (RWE) is increasingly used and accepted by health technology assessment (HTA) bodies as supportive evidence to inform the approval of new technologies. However, the criteria driving RWE acceptance are often unclear.

This study aims to improve understanding of the role and value of RWE in HTA decision-making and outline the best practices in building real-world external control arms (ECAs).

A mixed approach of a targeted literature review and HTA expert interviews was applied. The HTA reports of ten selected technologies and the expert interviews from France, Germany, Italy, Spain, and the UK informed the criteria driving the acceptance of RWE. Overall, the UK and Spanish HTA bodies are more receptive to accepting RWE, whereas the French and German are the least accepting. When RWE is used to substantiate efficacy claims, the level of scrutiny from regulators and HTA bodies is considerably higher than when RWE has different intended uses. Representativeness of the data source, overall transparency in the study and robust methodologies are the key criteria driving RWE acceptance across markets.

Using a laboratory experiment, we investigate complexity in decision problems as a cause of failures in contingent reasoning. For this purpose, we introduce three dimensions of complexity to a decision problem: the number of contingencies, the dominance property of choices, and reducible states. Each decision problem is designed to reflect variations in complexity across the three dimensions. Experimental results show that the number of contingencies has the most significant effect on failures in contingent reasoning. The second dimension, the dominance property of choices, also has a statistically significant effect, though the effect size is smaller than in the existing literature. In contrast, the third complexity dimension has no impact; presenting the decision problem in a reduced or reducible form does not change subjects’ performance on contingent reasoning. Additionally, we examine the Power of Certainty and show its existence. This effect is particularly pronounced when the number of contingencies is large.

We measured brain activity using a functional magnetic resonance imaging (fMRI) paradigm and conducted a whole-brain analysis while healthy adult Democrats and Republicans made non-hypothetical food choices. While the food purchase decisions were not significantly different, we found that brain activation during decision-making differs according to the participant’s party affiliation. Models of partisanship based on left insula, ventromedial prefrontal cortex, precuneus, superior frontal gyrus, or premotor/supplementary motor area activations achieve better than expected accuracy. Understanding the differential function of neural systems that lead to indistinguishable choices may provide leverage in explaining the broader mechanisms of partisanship.