Abstract Traditional well-being surveys reach policymakers months too late to guide effective intervention, leaving governments reactive rather than responsive to evolving citizen needs. This challenge is particularly acute in the Global South, where overlapping crises demand agile policy responses, but monitoring infrastructure remains limited. This study demonstrates that machine learning can transform readily available digital signals into reliable forecasts of population life satisfaction, offering a pathway to continuous well-being monitoring where it is most urgently needed. Validated using UK pandemic-era data, our framework shows that both mood indicators and search behaviors can accurately predict life satisfaction, achieving R² = 0.72 (survey-based) and R² = 0.623 (digital behavioral). Here, “continuous” denotes weekly population-level monitoring; “interpretable” reflects the use of an explainable Least-Square Boosting Regression (LSBoost) model providing feature-importance outputs; and “empirical” indicates validation on real-world, nationally representative survey and digital behavioral data. Digital approaches offer promise for resource-constrained settings. The system is explicitly designed for adaptation across diverse cultural contexts, addressing critical challenges of digital equity, cultural specificity, and algorithmic sovereignty while maintaining transparency and local control. By enabling continuous monitoring at low cost, this work empowers governments and civil society organizations to transition from crisis management to anticipatory governance—responding to citizen well-being as it evolves rather than after damage is done.

Gambling marketing on social media in countries like Great Britain (GB) is relatively well understood. Little is known, however, about how such marketing is impacted by major changes to the gambling landscape, like the COVID-19 pandemic. Here, we assessed changes in the frequency, sentiment, and content of gambling marketing on Twitter by Great Britain (GB) gambling operators and affiliates. We analysed n = 353,134 tweets from 10 operators and affiliates posted between January 2020 and July 2022. Using machine learning, we categorised tweets based on content and tracked how social media use by operators and affiliates changed during the pandemic. Findings revealed decreases in the frequency of tweets posted during the first national lockdown, particularly for affiliates, and a greater proportion of sports content related tweets, compared to direct advertising, as the pandemic continued. Postings by affiliates tended to include more positive sentiments. Our findings highlight the speed at which gambling operators and affiliates adapted their social media marketing campaigns to large structural changes like the COVID-19 lockdowns.

This paper explores the implications of generative artificial intelligence (GenAI) for research in the social sciences. We argue that GenAI is not merely a technical tool, but an epistemic agent that transforms how knowledge is produced, interpreted, and disseminated. Drawing on recent developments in large language models, image generators, and multimodal systems, we identify four key methodological opportunities—qualitative analysis, theory-building through simulation, visual and multimodal research, and collaborative writing. We then examine the risks and tensions these opportunities present, including algorithmic bias, hallucination, and the automation of interpretive judgment. In response, we outline a pedagogical and research agenda for critical AI literacy, ethical infrastructure, and methodological pluralism. Our goal is to initiate a reflexive, interdisciplinary conversation about how GenAI can be responsibly and creatively integrated into the social sciences.

Monitoring population-level life satisfaction is critical for effective and responsive policy planning, yet in many regions of the Global South, this remains a major challenge due to limited or delayed survey infrastructure. This study demonstrates that machine learning models can reliably predict life satisfaction using a combination of mood-based survey data and digital behavioural signals from search engine keyword trends. Applying least-squares boosting ensembles to UK longitudinal data (2020–2024), we find that mood indicators explain 72% of variance (R² = 0.72), while Google Trends search data achieves an inferior but notable accuracy from digital behavioural indicators alone (R² = 0.623) Although developed in a UK context, the framework is designed for adaptation to majority world settings, with low computational requirements, mobile-first compatibility, and a modular structure. We also address key ethical and technical considerations—such as the digital divide, cultural specificity, and data sovereignty—to support equitable implementation. This work offers a scalable, low-cost pathway to real-time well-being monitoring and agile policy response, empowering governments and NGOs with culturally grounded, data-driven insight where it is most urgently needed.

BackgroundThe coronavirus disease (COVID-19) pandemic has led to a dramatic increase in online searches related to psychological distress. Governments worldwide have responded with various measures to mitigate the impact of the virus, influencing public behavior and emotional well-being. This study investigated the relationship between government actions and public reactions in terms of online search behaviors, particularly concerning psychological distress during the pandemic. The primary objective of this study was to analyze how changes in government policies during the COVID-19 pandemic influenced public expressions of psychological distress, as reflected in the volume of related online searches in Kuwait.MethodUtilizing Google Trends data, the study analyzed search frequencies for terms associated with psychological distress such as “anxiety” and “lockdown.” The analysis correlated these search trends with government actions using the Oxford COVID-19 Government Response Tracker (OxCGRT). The study period covered March 1, 2020, to October 10, 2020, and involved extensive data collection and analysis using custom software in R programming.ResultsThere was a significant correlation between the stringency of government-imposed restrictions and the volume of online searches related to psychological distress. Increased searches for “lockdown” coincided with heightened government restrictions and were associated with increased searches for “anxiety,” suggesting that policy measures significantly impacted public psychological distress.ConclusionThe study concludes that governmental responses to the COVID-19 pandemic, measured through OxCGRT, have a measurable impact on public psychological distress, as evidenced by online search behaviors. This underscores the importance of considering psychological impacts in policymaking and suggests further research to explore this dynamic comprehensively. Future studies should focus on refining the correlation between specific types of policy measures and different expressions of psychological distress to better inform public health strategies and interventions.

There are many ongoing calls for the integration of public welfare concerns into engineering curricula, for example promoting social consciousness, human-centred design, and other socially-related frameworks. However, some engineering students still seem to devalue or resist these initiatives. This paper explores a new methodology to facilitate such integrations, with the intention of bypassing the possible resistance to considering non-technical, socially-orientated aspects, by exploiting a psychology-informed approach of priming. As priming holds the potential of inducing empathy (a prerequisite to human-centred designing practices, and a precondition to consciousness), and bypassing 'disruptive transitional behaviour', we test to see if we can prime civil engineers into human-centred designing. Students' levels of self- and social- awareness and consciousness (which are also factors contributing to engineering professional formation), were recorded before and after their engagement with our version of a Human-Centred Design Task. The effect of priming on these indicators was also captured. No significance in the before versus after results was found, and results showed no significant impact of the priming on the Self-Awareness Indicators. However, there were unexpected results of students' levels of Social-Awareness Indicators. Students' levels of Social Consciousness were shown to have significantly decreased (rather than increased) due to the priming. The results led to further expanding the literature review to seek a possible explanation. We discuss possible reasons behind these results, linking their decrease due to the priming, to the self-enhancing, agentic personal engineering values. These values appear to have a contribution towards decision making (and thus, problem solving), and an influence on the students'/ designers' engagement with empathy (which is a prerequisite to human-centred designing). This sheds light on the need to expand the research on the topic of engineering personal values, and their possible influence on human-centred design, and other socially-related design processes and factors.

Background:Whilst some research has explored the impact of COVID-19 on gambling behaviour, little is yet known about online search behaviours for gambling during this period. The current study explored gambling-related online searches before, during and after the outbreak of the COVID-19 pandemic in the UK. We also assessed whether search trends were related to Gambling Commission behavioural data over the same period. Methods:Google Trends™ search data, covering thirty months from January 2020 to June 2022, for five gambling activities and five gambling operators were downloaded. Graphical displays of the weekly relative search values over this period were then produced to visualise trends in search terms, with key dates in COVID-19 policy and sporting events highlighted. Cross-correlations between seasonally adjusted monthly search data and behavioural indices were conducted. Results:Sharp increases in internet searches for poker, slots, and bingo were evident during the first lockdown in the UK, with operator searches sharply decreasing over this period. No changes in gambling activity searches were highlighted during subsequent lockdowns, although small increases in operator-based searches were detected. Strong positive correlations were found between search data and industry data for sports betting and poker but not for slots. Conclusions:Google Trends™ data may act as an indicator of population-level gambling behaviour. Substitution of preferred gambling activities for others may have occurred during the first lockdown when opportunities for sports betting were limited. Further research is needed to assess the effectiveness of internet search data in predicting gambling-related harm.

The unfolding of the COVID-19 outbreak was an unprecedented and unanticipated opportunity to understand how a sudden global shock modulates people's online searches when seeking information about their emotional well-being. It has also illustrated how public health surveillance systems were essential for tracking diseases' spatial and temporal dynamics and shaping rapid public policy changes. The present paper validates a data mining and processing framework which examines how digital epidemiology and machine learning reveal trends in human mental health and psychological distress expression variability. We present results obtained in two research exploring the relationship between Google Trends time-series in the digital surveillance of search engines during the pandemic and a selection of social media feeds and official UK well-being surveys. The generated body of evidence shows how data science can provide robust, finely grained, and replicable evidence on mental health measures at the population level. In the future, the digital surveillance analytics validated here can be rapidly deployed for crisis management and allow early detection of distress signals to better manage communication and policy action at population level.

The spinal structures found on Copiapoa cinerea var. haseltoniana, an efficient dew-harvesting cactus, were fabricated and evaluated both in a climate chamber and outdoors in dewy conditions. A mix of aluminium and steel was used to fabricate these surfaces, with aluminium being used for everything but the replicated spine features, which were constructed from steel. Each surface was entirely coated with a highly emissive paint containing an alumina–silicate OPUR additive. Three replica versions (stem only, spine only, and stem & spine) were compared to a flat planar reference surface. Experimental results demonstrated that all three biomimetic macro-structured surfaces significantly enhanced dew harvesting compared to the reference surface. It was established that the stem & spine replica, spine replica, and stem replica all demonstrated significantly more dew harvesting, with mean efficiency ratios in respect of the reference surface of 1.08 ± 0.03, 1.08 ± 0.02, and 1.02 ± 0.01, respectively. Furthermore, the method of surface water collection was found to influence the water collection rate. The diagonal run-off flow across a flat planar surface was 34% more efficient than the parallel run-off flow on the same surface. These findings provide valuable insights for the construction and installation of biomimetic-inspired dew-harvesting devices, particularly in regions that are most challenged by decreasing dew yields as a result of climate change.

Perfectionism is a personality trait associated with a desire for flawlessness, high-standard expectations and criticism of the self and others. As engineering design seeks to address more wicked problems that move beyond technical considerations, it is possible that engineers with perfectionism may struggle to engage flexibly with complexity and more creativity-focused solutions. The present study seeks to understand perfectionism prevalence in an undergraduate cohort of civil engineers and the impact of this trait on complex design decisions and engagements that include social as well as technical considerations. 184 civil engineering students were involved in this study. We found that 74.5% of the engineers classify as perfectionists, with 68.5% of these perfectionists being maladaptive. Further, we examined how perfectionism associated with Communal Designs, a design approach that aims to meet physical community needs as well as more metaphysical, empathy-informed criteria. We found that although perfectionists were more likely to have higher scores of prosocialness and empathy, non-perfectionists were more likely to produce Communal Designs. This suggested an apparent intention-behaviour mismatch. Engineering students may have intended to but then failed to produce Communal Designs; this could also be explained via our finding that perfectionists tend to have higher social desirability scores. The results indicate that complex decision-making in engineering design cannot be separated from the mindsets and personalities of engineers. Strategies to mitigate the negative impact of perfectionism are discussed, including both supported exposure to open-ended, contextualised design, and the use of critical reflection. A regression model predictive of Communal Design production was also developed and discussed using engineering undergraduates’ personality characteristics’ scores as predictors.

General public’s mental health can be affected by the public policy response to a pandemic threat. Britain, Italy and Sweden have had very distinct approaches to the COVID-19 pandemic: early lock-down, delayed lock-down and no-lock-down. We develop a novel narrative economics of language Culture-Based Development approach, and using Google trend data for seed keywords, death and suicide, we reach two main conclusions: (i) while countries had a pre-existing culturally relative disposition towards death-related anxiety, the sensitivity to the public policy towards COVID-19 was also country specific; (ii) however, significant spillovers from one specific national lockdown public policy to another country’s mental health are identified.

An online world exists in which businesses have become burdened with managerial and legal duties regarding the seeking of informed consent and the protection of privacy and personal data, while growing public cynicism regarding personal data collection threatens the healthy development of marketing and e-commerce. This research seeks to address such cynicism by assisting organisations to devise ethical consent management processes that consider an individual’s attitudes, their subjective norms and their perceived sense of control during the elicitation of consent. It does so by developing an original conceptual model for online informed consent, argued through logical reasoning, and supported by an illustrative example, which brings together the autonomous authorisation (AA) model of informed consent and the theory of planned behaviour (TPB). Accordingly, it constructs a model for online informed consent, rooted in the ethic of autonomy, which employs behavioural theory to facilitate a mode of consent elicitation that prioritises users’ interests and supports ethical information management and marketing practices. The model also introduces a novel concept, the informed attitude , which must be present for informed consent to be valid. It also reveals that, under certain tolerated conditions, it is possible for informed consent to be provided unwillingly and to remain valid: this has significant ethical, information management and marketing implications.

AbstractBackgroundNeurobehavioral research on the role of impulsivity in gambling disorder (GD) has produced heterogeneous findings. Impulsivity is multifaceted with different experimental tasks measuring different subprocesses, such as response inhibition and distractor interference. Little is known about the neurochemistry of inhibition and interference in GD.MethodsWe investigated inhibition with the stop signal task (SST) and interference with the Eriksen Flanker task, and related performance to metabolite levels in individuals with and without GD. We employed magnetic resonance spectroscopy (MRS) to record glutamate–glutamine (Glx/Cr) and inhibitory, γ-aminobutyric acid (GABA+/Cr) levels in the dorsal ACC (dACC), right dorsolateral prefrontal cortex (dlPFC), and an occipital control voxel.ResultsWe found slower processing of complex stimuli in the Flanker task in GD (P < .001,η2p = 0.78), and no group differences in SST performance. Levels of dACC Glx/Cr and frequency of incongruent errors were correlated positively in GD only (r = 0.92,P = .001). Larger positive correlations were found for those with GD between dACC GABA+/Cr and SST Go error response times (z = 2.83,P = .004), as well as between dACC Glx/Cr and frequency of Go errors (z = 2.23,P = .03), indicating general Glx-related error processing deficits. Both groups expressed equivalent positive correlations between posterror slowing and Glx/Cr in the right dlPFC (GD:r = 0.74,P = .02; non-GD:r = .71,P = .01).ConclusionInhibition and interference impairments are reflected in dACC baseline metabolite levels and error processing deficits in GD.

The education sector is crucial to any nation committed to building future human capital. The Higher Education sector in the Kingdom of Saudi Arabia (KSA) is at the centre of transforming the nation's future in a radical move to end oil-dependency. But this is only possible if universities make a decisive change and start working as learning organisations in all employee's levels. The present study investigates the direction of higher education in becoming learning organisations. We collected data from 840 staff members in 20 public Saudi universities. We designed a questionnaire exploring the seven dimensions of learning organisation found in the literature.  Regression analyses were used to assess the impact of those dimensions on the organisational performance. Results showed that universities that adhered most to the learning organisation principles demonstrated a better organisational performance, particularly concerning research and knowledge performance. We recommend that universities should (1) use change agents to help transform effectively and meet rising demands and (2), promote continuous learning for all employees to increase productivity.

Studies show that personal values can influence decision making, problem solving, and behaviour. We draw from this literature and analyse the link between personal value and designs produced by civil engineering students, as part of a Human-Centred Designing assignment. We also study the influence of priming on design decisions. We collected data on Schwartz’s Personal Value Systems of first- and third year civil engineering students at a university in Wales. Students were set a conceptual design task to fulfil a variety of human needs from subsistence to freedom, with the intention of elevating the quality of life of residents by meeting as many needs as possible. We analysed which Higher Order Values were more likely to produce designs with community-orientated spaces that enable residents to interact, fulfilling communal needs, termed ‘Communal Designs’. While the majority (63.93%) of first year students were in the Higher Order Value Self Transcendence category, which is aligned with communal values, only 27.78% of them produced a Communal Design, with 50% of these having higher-than-average social desirability scores. On the other hand, the majority of Communal Designs (73.33%) were produced by those in the Higher Order Value Openness to Change category, with only 18.18% of these having higher-than-average social desirability scores. These findings lead us to either doubt the accuracy of the claimed Higher Order Value of the majority of civil engineering students, or require us to make sense of the dissonance between proclaimed values held, and the lack of acting upon it to produce Communal Designs. Priming had no significant effect on whether a student produced a Communal Design, although it seemed to have a significant decreasing influence on Empathic Concern, which is associated with prosocial, altruistic, self-transcendent acts. Our study also shows that the majority (54.84%) of third year students, also had their primary Higher Order Value as Self Transcendence. Comparative analyses were run to search for differences in personal value systems between the first year and third year civil engineering students. It was found that third year students valued Tradition more than first year students. Tradition ultimately contributes toward the Higher Order Value of Conservation, which is opposed to Openness to Change, and thus the likelihood of a student producing a Communal Design. First year students had a significant correlation between their Basic Value of Tradition and their Higher Order Value of Self Enhancement, and between Tradition and their Higher Order Value of Openness to Change. Third year students were found to have a significant correlation between Tradition and their Higher Order Value of Self Transcendence. This is an interesting finding, given that Self Enhancement and Self Transcendence are opposing in nature, and that there has been discussion of how cultural values could change within engineering education over time. We also discuss whether Sheeran & Web’s ‘Intention - Behaviour Gap’ could offer an explanation of the dissonance between the Higher Order Value and the decision to act in accordance with it (for example, a Higher Order Value of Self Transcendence, a communal value, was hypothesised to lead to designs promoting community, but this did not occur). In taking this forward, the principles behind identifying Communal Designs were found to align to ‘Placemaking’, a term used in architectural urban design to cultivate spaces for community engagement. We propose that Placemaking could be integrated into civil engineering’s conceptual design education, as it may provide a framework for civil engineers to consider social impact of design.

This study investigates the factors that build resistance and attitude towards AI voice assistants (AIVA). A theoretical model is proposed using the dual-factor framework by integrating status quo bias factors (sunk cost, regret avoidance, inertia, perceived value, switching costs, and perceived threat) and Technology Acceptance Model (TAM; perceived ease of use and perceived usefulness) variables. The study model investigates the relationship between the status quo factors and resistance towards adoption of AIVA, and the relationship between TAM factors and attitudes towards AIVA. A sample of four hundred and twenty was analysed using structural equation modeling to investigate the proposed hypotheses. The results indicate an insignificant relationship between inertia and resistance to AIVA. Perceived value was found to have a negative but significant relationship with resistance to AIVA. Further, the study also found that inertia significantly differs across gender (male/female) and age groupings. The study's framework and results are posited as adding value to the extant literature and practice, directly related to status quo bias theory, dual-factor model and TAM.

The UK has experienced substantial income and wealth inequalities at the individual and regional levels. The coronavirus disease 2019 (COVID-19) pandemic reveals how inherited domestic economic and cultural disparities lead to greater vulnerability, even in front of the 'Great Leveller'. We argue that the geography of the pandemic in the UK follows the geography of deprivation and cultural and economic discrimination that existed before the pandemic. We demonstrate this through analysis of multiple deprivation and cultural (ethnic) concentration data, unemployment claims, and small business statistics, as well as lung cancer deaths in non-pandemic times and weekly death statistics during the early part of the pandemic (3/1/2020 until 27/03/2020) across England and Wales. We apply data decomposition analysis to detect this discrimination and map it against the geography of the pandemic. Our study not only illustrates the geography of the pandemic disease but also demonstrates how past cultural and economic discrimination creates vulnerable groups and places among the general public in times of exogenous shocks. Finally, we discuss our findings in light of the emerging impact of Brexit.

Particulate matter (PM) in ambient air is associated with many adverse health outcomes. Although many anthropogenic activities are associated with PM release in indoor settings, dispersion and persistence of PM is poorly understood. In this study, concentration, persistence and dispersion of PM2.5 and PM10 released following aerosol antiperspirant use were measured in a bathroom environment under several door and window ventilation conditions, and in a nearby bedroom. Daily mean concentrations were elevated in all experimental conditions compared to the control, but varied depending on the ventilation condition. The daily mean concentrations exceeded the WHO daily mean guideline values when there was little or insufficient ventilation in the bathroom, whereas ventilation through opening doors or windows prevents exceedances. After spraying, mean peak PM concentrations were lowest in the bathroom when the door and window were left open. Introducing ventilation through opening the bathroom door and/or window reduced PM concentrations by > 93% 10 min after spray release, compared to reductions of 60% and 77% for PM2.5 and PM10, respectively, with no ventilation. Opening the bathroom window significantly increased peak PM concentration in the bedroom relative to leaving the window closed, suggesting increased dispersion of PM from bathroom to bedroom.

EDITORIAL article Front. Neurosci., 14 February 2020Sec. Neural Technology Volume 14 - 2020 | https://doi.org/10.3389/fnins.2020.00053