The editors, Afia Nyarko Boakye, Ralph Nyadu-Addo, Sycil Ayesu Gyimah and Lady Rachel Akosua Lundgren, bring together perspectives from entrepreneurship research, management studies, gender studies and economic development. Their stated aim is to move the conversation beyond simple descriptions of inequality and toward an integrated account of the mechanisms that produce it. That matters because gender-based disparities in entrepreneurship are not the product of any single obstacle. They emerge from the interaction of financial systems, market structures, cultural expectations and institutional rules, each of which can reinforce the others. A woman entrepreneur who lacks collateral for a loan may also lack the professional networks that connect founders to investors, and the stereotypes that shape both of those deficits may trace back to socio-cultural norms about who belongs in business leadership at all.

Access to finance is one of the most extensively documented barriers the book examines. Research across many economies has repeatedly found that women-led ventures receive a disproportionately small share of venture capital, bank lending and other formal financing, a pattern that constrains growth from the earliest stages. The volume situates this problem within broader theories of gender, asking how financial institutions evaluate risk, how collateral requirements interact with unequal property ownership, and how investor perceptions of competence and ambition are filtered through gendered expectations. By framing finance as a structural issue rather than a matter of individual confidence or preparation, the book aligns itself with a growing body of scholarship that treats funding gaps as symptoms of systemic design rather than personal shortcoming.

Market access presents a parallel challenge. Even well-capitalized ventures depend on customers, supply chains and distribution channels, and the book explores how gender can shape entry into each of these. Socio-cultural norms and stereotypes influence not only how entrepreneurs are perceived by buyers and partners but also which industries and business models are considered appropriate or credible for them to pursue. The editors also give sustained attention to leadership, examining how expectations about authority, communication and decision-making affect women who rise to lead their own companies. Resilience, mentorship and professional networking receive their own treatment, reflecting the recognition that entrepreneurial success is rarely a solo achievement. Founders who can draw on experienced mentors and well-connected networks consistently outperform those who cannot, and the book asks why such resources are unevenly distributed in the first place.

One of the more forward-looking sections of the volume addresses digital transformation and the opportunities it creates for women entrepreneurs. Digital platforms, e-commerce infrastructure and remote business tools have lowered some traditional barriers to entry, allowing founders to reach customers and suppliers without the physical presence or institutional relationships that older business models demanded. The book treats this as a genuine but conditional opportunity. Digital transformation can widen participation, yet it can also reproduce existing inequalities if access to technology, digital skills and online financial services remains uneven. The editors’ framing suggests that technology is best understood as an amplifier: it can magnify inclusion where the underlying ecosystem supports it, and magnify exclusion where it does not. That nuance distinguishes the volume from more celebratory accounts of the digital economy.

Policy and institutional frameworks form another pillar of the analysis. The book examines how laws, regulations, procurement rules and entrepreneurship support programs can be designed to promote inclusion rather than merely tolerate it. This includes practical strategies for fostering equity, innovation and sustainable economic growth, from reforming lending practices to building mentorship pipelines and embedding gender considerations in the mandates of development institutions. The editors argue that inclusive entrepreneurial ecosystems are not simply a matter of fairness; they are an economic imperative. When a large share of the population faces structural barriers to founding and growing businesses, economies lose access to ideas, jobs and productivity gains that those ventures would otherwise generate. Entrepreneurship, as the book emphasizes, plays an important role in innovation, employment and economic development, and any friction that suppresses participation carries a measurable cost.

The volume’s methodological breadth is one of its distinguishing features. Rather than relying on a single disciplinary lens, it draws on entrepreneurship theory, management research, gender studies and development economics, combining scholarly perspectives with contemporary examples. This multidisciplinary approach reflects the composition of the editorial team itself, whose expertise spans gender, entrepreneurship, management, inclusion and economic development. For readers, the practical effect is a treatment of the subject that connects abstract theory to observable outcomes: gender theories are linked to patterns of entrepreneurial participation, and institutional analysis is tied to concrete policy levers. The book is aimed at researchers, academics, undergraduate and postgraduate students, policymakers, business leaders, entrepreneurship support organizations and development practitioners, a readership that mirrors the range of actors whose coordination the authors appear to consider necessary for change.

The emphasis on balance is notable. Discussions of gender and business often polarize between narratives of unqualified progress and accounts of entrenched discrimination, and the volume positions itself deliberately between these poles. It documents real barriers to finance, market access, leadership and business opportunity while simultaneously highlighting pathways toward more inclusive and equitable entrepreneurial ecosystems. The inclusion of emerging opportunities, particularly those created by digital transformation, signals that the editors see the current moment as one of genuine possibility rather than stagnation. The book’s structure, moving from theory through barriers to strategies and future directions, gives readers a framework for understanding not only what is wrong but what has worked and what could work under the right institutional conditions.

What the volume ultimately contributes is a map. It identifies where the obstacles to gender-inclusive entrepreneurship cluster, in financial systems, in market structures, in social norms and in institutional design, and it catalogs the interventions that research suggests can loosen them. For policymakers, the message is that inclusion requires deliberate architecture: procurement rules, funding mechanisms and support programs designed with evidence of how gender operates in entrepreneurial settings. For researchers, it offers an integrated agenda that connects previously siloed strands of inquiry. For founders and the organizations that support them, it provides language and evidence for the barriers they encounter daily. As debates over equity, innovation and economic growth intensify worldwide, the book’s central argument, that entrepreneurial ecosystems are built choices rather than natural outcomes, and can therefore be rebuilt more inclusively, is likely to find a wide and receptive audience.

Subject of Research: Gender-based barriers to entrepreneurship and strategies for building inclusive entrepreneurial ecosystems

Article Title: Gender and Entrepreneurship: Navigating Barriers, Building Inclusion and Shaping the Future

Article References: Gender and Entrepreneurship: Navigating Barriers, Building Inclusion and Shaping the Future. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: gender, entrepreneurship, access to finance, market access, leadership, mentorship, networking, digital transformation, inclusion, policy frameworks, economic development, women entrepreneurs

The study, led by Md Ayatullah Khan and Farhana Hoque of Khulna University together with colleagues at the University of Chittagong, surveyed 450 slum household heads using a cluster-based household survey across three purposively selected wards — 12, 21 and 31 — of the Khulna City Corporation, an area home to a significant share of Bangladesh’s slum dwellers. Rather than treating pandemic hardship as a single outcome, the researchers modelled it as a chain of linked events: employment loss leading to income loss, income loss driving shortages of daily necessities, and those shortages in turn forcing families to deplete savings and, ultimately, sell off assets. Each link in that chain was analysed separately, allowing the team to trace precisely how one form of deprivation raised the odds of the next.

The statistical toolkit combined descriptive measures — means, standard deviations and percentages — with inferential techniques including t-tests, chi-square tests and a binary logit model. The binary logit approach, a standard method for modelling outcomes with two possible states, allowed the researchers to estimate how demographic and socio-economic characteristics changed the probability that a household experienced each stage of the loss spiral. This design matters because it moves the analysis beyond simple description: it identifies which characteristics of households statistically predicted each downstream loss, controlling for the other variables in the model.

The demographic profile that emerged from the survey is strikingly consistent with the structure of urban informal labour in South Asia. The majority of household heads were male, had little formal education, and worked in daily-wage or informal occupations — day labourers, market vendors, rickshaw pullers, auto drivers and housemaids. These are precisely the jobs that vanished overnight when lockdowns were imposed. A rickshaw puller cannot work from home; a market vendor cannot sell vegetables to customers who are ordered to stay indoors. Unlike salaried workers in the formal economy, informal workers in Bangladesh typically have no contracts, no severance pay and no unemployment insurance, so the loss of work translated immediately and completely into the loss of income.

And that translation was the engine of the spiral. The analysis found that employment loss strongly predicted income loss, which in turn significantly increased the odds that a household would face shortages of daily needs — food, medicine and other essentials. Once a family’s income stream was severed, its thin financial buffers eroded rapidly. Savings loss followed, and beyond savings came asset loss: the sale of livestock, utensils, mobile phones or building materials that represented years of accumulated economic security. In the vocabulary of development economics, these households were not merely made temporarily poorer; they were pushed backward along the asset ladder that normally protects poor families from falling into chronic poverty.

The binary logit results also isolated the risk factors that made some households more likely to suffer than others. Older age of the household head, low levels of schooling, pre-existing indebtedness and living in rented accommodation all emerged as key predictors of pandemic-induced losses. Each of these factors has a clear mechanistic interpretation. Older household heads face both reduced earning capacity and greater health risks in a pandemic, making them less able to seek alternative work. Low schooling restricts the range of jobs a person can take, trapping workers in the most pandemic-exposed occupations. Indebtedness means that any income interruption immediately triggers pressure from lenders, forcing distress sales. And renters, unlike owners, must continue paying for shelter even when their income has stopped, converting every week of unemployment into a mounting liability.

The authors interpret these findings through the lens of the livelihood-vulnerability framework and the concept of the poverty trap. In this view, poverty is not simply a low level of income but a dynamic process in which shocks interact with a household’s limited assets, weak institutions and precarious employment to produce self-reinforcing decline. A pandemic lockdown is exactly the kind of shock that such households are least equipped to absorb: they hold few liquid assets, they lack access to formal credit, and their labour — their principal asset — is the very thing the lockdown suspends. The result is a vicious circle in which each loss makes the household more vulnerable to the next, and recovery becomes progressively harder without external intervention.

The Khulna findings echo a broader pattern documented across the Global South during the pandemic. Studies of informal settlements in Bangkok, Kampala, Rio de Janeiro, Nairobi and Santiago de Chile all recorded steep employment and income losses among slum residents, along with food insecurity and disrupted access to healthcare. Research in Dhaka and elsewhere in Bangladesh reported income drops of as much as 80 percent among the urban poor during the early lockdowns. What the new study adds is the explicit modelling of the loss cascade itself — demonstrating statistically that the stages of deprivation are not parallel symptoms of the pandemic but sequential consequences, with employment loss as the upstream driver and asset loss as the terminal stage.

From this causal structure, the authors derive a set of policy recommendations that are notable for their sequencing. Because employment loss sits at the head of the spiral, interventions that prevent job loss or rapidly restore earning capacity deliver the greatest downstream benefit. The study recommends a targeted skills development programme and the creation of formal employment linkages for informal workers, alongside legal and institutional protections for informal work — a sector that employs the vast majority of urban workers in Bangladesh but operates largely outside the reach of labour regulation. Such measures address the structural fragility that made slum dwellers so exposed in the first place.

For households already caught in the spiral, the authors recommend immediate cash transfers, debt relief and rental support. Cash transfers replenish the income stream directly, allowing families to buy food without selling assets; debt relief halts the distress-sale dynamic that converts temporary hardship into permanent impoverishment; and rental support keeps families housed while they rebuild. The logic is to interrupt the cascade at its weakest link before the losses compound. As the world confronts future pandemics, climate shocks and economic crises — all of which hit informal urban settlements hardest — the Khulna study offers a sobering lesson: protecting the poor requires not just aid, but aid delivered early enough, and targeted precisely enough, to stop the spiral before it starts.

Subject of Research: Socio-economic drivers of pandemic-induced employment, income, savings and asset losses among urban slum dwellers in Khulna, Bangladesh

Article Title: Drivers of pandemic-induced losses and shortages among urban slum dwellers: a case from Bangladesh

Article References: Khan, M. A., Hoque, F., Islam, K. R., & Yeasmin, K. M. (2026). Drivers of pandemic-induced losses and shortages among urban slum dwellers: a case from Bangladesh. SN Social Sciences, 6(10), Article 444. https://doi.org/10.1007/s43545-026-01729-6

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01729-6

Keywords: COVID-19, urban slums, Bangladesh, informal employment, income loss, poverty trap, livelihood vulnerability, binary logit model, cash transfers, Khulna, food insecurity, debt relief

The study, conducted by Wenyi Wang of the University of Texas at Dallas, drew on the Youth Risk Behavior Survey, a biennial school-based surveillance system run by the Centers for Disease Control and Prevention since 1990. The researcher pooled data from five survey waves spanning 2015 to 2023, an initial sample of 81,401 U.S. high school students. After restricting the analysis to respondents with complete information on the key measures, 20,367 adolescents remained. That substantial attrition was a deliberate trade-off: the author prioritized internal validity, accepting that the final sample disproportionately excluded Black students, younger teens, and those in earlier grades, in exchange for a dataset in which every confounder could be fully accounted for.

What sets this analysis apart technically is its use of overlap propensity score weighting, a statistical technique designed to mimic the balance achieved in a randomized controlled trial. Rather than simply adding covariates to a regression model, the study estimated each adolescent’s probability of belonging to a given sexual orientation group based on more than 40 suicidality-related characteristics, including demographics, sleep and eating patterns, physical activity, substance use, risky sexual behaviors, violence exposure, and risk-seeking behaviors such as texting while driving or carrying weapons. Overlap weights then re-weighted the sample into a pseudo-population in which heterosexual, bisexual, homosexual, and unsure-orientation youth were statistically comparable on all measured variables. Among several weighting schemes tested, overlap weights performed best, driving all absolute standardized differences below the conventional 0.10 threshold for adequate balance.

The descriptive picture that emerged was sobering. In the analysis sample, 24.25 percent of adolescents reported seriously considering suicide in the past 12 months, 16.31 percent had made a suicide plan, 28.78 percent reported at least one attempt, and nearly 29 percent experienced persistent sadness or hopelessness lasting two weeks or more. Before any adjustment, bisexual youth stood out as the highest-risk group across nearly every domain: they reported the highest rates of suicidal ideation, planning, attempts, and sexual victimization, along with elevated substance use and risky sexual behaviors. Homosexual students experienced the highest levels of non-sexual victimization, while adolescents unsure of their orientation also showed elevated risk profiles across multiple domains.

After weighting, the disparities in suicidal thinking remained striking. Bisexual, homosexual, and unsure-orientation youth all showed significantly higher adjusted odds of suicidal ideation and planning compared with heterosexual peers, with odds ratios ranging from 2.06 to 4.19. Homosexual identity was associated with the highest adjusted odds of ideation (3.41) and planning (4.19) once victimization and hopelessness were accounted for. Bisexual youth were 26.7 percent more likely than heterosexual youth to report suicidal ideation even after balancing the full covariate set. Notably, however, the pattern for actual suicide attempts was far less consistent, with adjusted odds ratios ranging from 0.89 to 1.46 across groups, and unsure-orientation youth actually showing lower odds of attempts than heterosexual peers.

The study’s theoretical framing drew on general strain theory and its extension, queer general strain theory, which hold that victimization is the most consequential external strain producing negative emotions and maladaptive coping, including suicidality. This framework, combined with minority stress theory, suggests that sexual minority youth are not inherently prone to suicide; rather, their elevated rates reflect a disproportionate burden of stigma-related stressors. The analysis also confronted a competing explanation: some researchers argue that genetic predispositions, adverse childhood experiences, or developmental vulnerabilities that predate identity formation may partly account for the disparities. By balancing an unusually wide array of observed confounders, the study sought to narrow the space for such alternative accounts, though it acknowledged that unmeasured biological and early-life factors cannot be ruled out.

The central analytical question concerned moderation: does the link between victimization or hopelessness and suicidality differ by orientation? Across all groups, both stressors were robustly associated with suicidal ideation and planning, with odds ratios between 1.73 and 3.32. But the interaction tests told a more complicated story. For bisexual youth, the association between total victimization and suicide outcomes was significantly attenuated compared with heterosexual peers, a buffering pattern that also held for non-sexual victimization among unsure-orientation youth. Yet for homosexual youth, the picture reversed depending on the stressor type: homosexual identity buffered the link between non-sexual victimization and suicide planning, but amplified the association between sexual victimization and all suicide outcomes, and strengthened the link between hopelessness and actual attempts. Sexual victimization appeared particularly toxic for unsure-orientation youth, exacerbating risk for ideation and attempts.

An exploratory supplementary analysis using the 2023 survey, the only year measuring transgender identity, offered preliminary insights into an especially vulnerable population. Among 5,819 complete cases, transgender youth showed dramatically elevated adjusted odds of suicidal ideation (2.73) and planning (5.21) compared with heterosexual youth, yet paradoxically lower odds of attempts (0.34). Victimization showed weaker associations with suicide outcomes among transgender youth, while hopelessness was more strongly correlated with ideation and planning. The author emphasized that these transgender-specific estimates carry wide confidence intervals due to the small subgroup size and limited covariate overlap, and should be interpreted with considerable caution.

Perhaps the most consequential finding is what the interactions did not show. Most interaction terms were statistically non-significant and inconsistent in direction, suggesting that sexual minority youth do not possess a unique or heightened susceptibility to the effects of victimization and hopelessness. Instead, the disparities appear to be driven largely by differential exposure: bisexual, homosexual, and unsure youth simply encounter more victimization and more hopelessness than heterosexual peers. This distinction matters enormously for prevention, because it implies that reducing exposure to bullying, sexual violence, and discrimination could meaningfully narrow the gap, rather than requiring orientation-specific psychological interventions.

The study’s limitations temper these conclusions. The cross-sectional design cannot establish temporal order, and reverse causation remains possible, since chronically suicidal adolescents may be more likely to perceive and report victimization and hopelessness. Overlap weighting balances only measured covariates, leaving residual confounding by genetic liability, family processes, school climate, and structural stressors unaddressed. Sensitive self-reported measures are vulnerable to recall bias, social desirability, and differential nonresponse, with non-response on suicide attempts running higher among homosexual students than heterosexual ones, raising the possibility that the highest-risk respondents go uncounted. Sexual identity was measured by self-identification alone, without capturing attraction or partner gender. Still, sensitivity analyses using multiple imputation across 60,278 cases reproduced the complete-case findings, strengthening confidence in the core results. The author calls for longitudinal research incorporating richer contextual and biological measures to clarify the mechanisms that keep sexual orientation independently associated with suicidal thinking even after more than 40 risk factors have been balanced away.

Subject of Research: Sexual orientation differences in the associations of victimization and hopelessness with adolescent suicidality in U.S. Youth Risk Behavior Survey data

Article Title: Do Victimization and Hopelessness Relate to Suicidality Differently Across Sexual Orientation? Evidence from the 2015–2023 U.S. Youth Risk Behavior Surveys

Article References: Wang, W. (2026). Do Victimization and Hopelessness Relate to Suicidality Differently Across Sexual Orientation? Evidence from the 2015–2023 U.S. Youth Risk Behavior Surveys. Archives of Sexual Behavior. https://doi.org/10.1007/s10508-026-03534-x

Image Credits: AI Generated

DOI: 10.1007/s10508-026-03534-x

Keywords: suicidality, sexual orientation, bisexual youth, victimization, hopelessness, Youth Risk Behavior Survey, adolescent mental health, minority stress, propensity score weighting, transgender youth, suicide prevention, Archives of Sexual Behavior

The research, led by Mohd Zaidi Bin Amiruddin of Universitas Pendidikan Indonesia together with colleagues including Kristóf Fenyvesi of the Finnish Institute for Educational Research at the University of Jyväskylä, focused on a question that has occupied science education researchers for decades: how do you help a student not just memorize the right answer, but genuinely restructure the internal model they use to reason about a physical phenomenon? The team’s answer was a learning intervention they call RefTaR, short for refutational texts assisted by augmented reality, which embeds three-dimensional visualization directly into a text-based strategy for confronting misconceptions.

Refutational texts are a well-established tool in science education. Rather than simply presenting the correct explanation, a refutational text explicitly names the common misconception, acknowledges that it is intuitive and widespread, and then directly refutes it before offering the scientific account. Research stretching back over several decades has shown that this approach outperforms plain expository text when the goal is overturning deeply held intuitive ideas. The catch is that many physics misconceptions involve entities students cannot see at all. Telling a student that their idea about charge transfer is wrong is one thing; giving them something concrete to replace it with is another.

That is where the augmented reality component enters. The RefTaR materials incorporated a macro-micro model, explicitly linking the observable, macroscopic behavior of charged objects with the microscopic behavior of subatomic particles. Students reading the refutational passages could simultaneously view augmented reality renderings and three-dimensional video animations showing what happens at the level of electrons when objects are rubbed together, transferred charge redistributes, or neutral objects become polarized. The design reflects a broader principle from multimedia learning research: multiple representations are most effective when they are coordinated so that the learner can map one onto the other, rather than being presented as disconnected pieces of information.

To test whether this combination actually helped, the researchers ran a group-randomized alternative-treatments design with a pretest. Three available twelfth-grade classes were the pool, and two of them were randomly assigned to the experimental and control conditions. The experimental group, twenty-eight students, received the full RefTaR intervention. The control group, thirty-one students, received refutational texts alone, without the augmented reality and 3D video components. Both groups received the same total amount of instruction, 270 minutes delivered across two sessions, which means any difference in outcomes could be attributed to the nature of the materials rather than to extra time on task.

Measuring mental models is trickier than measuring test scores, and the team took a correspondingly careful approach. Students’ understanding was assessed across six static electricity subconcepts using an open-ended level-of-understanding test. Rather than forcing a binary right-or-wrong judgment, this instrument allowed the researchers to classify each student’s mental model into one of three categories: Initial, meaning the intuitive or everyday conception; Synthetic, a hybrid state where scientific and intuitive ideas coexist and compete; and Scientific, the fully accepted explanation. The open-ended responses were scored independently by three raters, who reached 97.9 percent agreement, corresponding to a Fleiss’ kappa of 0.98, an almost perfect level of inter-rater reliability that lends considerable weight to the classifications.

The results tell a nuanced story that the authors are careful not to overstate. When the researchers used Fisher’s exact tests to compare the proportion of students showing any improvement from pretest to posttest, the so-called acceptable change, they found no statistically significant difference between the RefTaR group and the refutational-text-only group. In other words, both interventions moved students in the right direction at roughly comparable rates, and the addition of augmented reality did not produce a general advantage in the overall likelihood of progress.

But when the researchers narrowed their focus to the most demanding outcome, the proportion of students who reached a fully scientific mental model at posttest, a clear and consistent difference emerged. Across all six static electricity subconcepts, the RefTaR group had a significantly higher proportion of students at the Scientific level, with risk differences ranging from 17.4 to 42.9 percentage points, all statistically significant. This pattern held for every subconcept examined, which is a striking degree of consistency for a classroom-based study with modest sample sizes.

The interpretation the authors offer is that RefTaR’s advantage lies specifically in the final, integrative stage of mental-model reconstruction. Moving a student from an Initial model to a Synthetic one, where scientific and intuitive ideas begin to compete, appears achievable with refutational text alone. But completing the journey, fully displacing the intuitive model so that the scientific one operates alone, seems to benefit from the kind of rich, coordinated visualization that augmented reality and 3D video provide. The microscopic world of electrons and charge distributions, rendered as manipulable three-dimensional objects overlaid on the student’s physical environment, may give learners the concrete anchor they need to consolidate a coherent scientific model rather than a fragile hybrid.

The study’s authors are candid about its limits and about the questions that remain. Because the RefTaR intervention bundled augmented reality with 3D video and a macro-micro representational structure, the design cannot isolate which specific element drove the advantage. It could be the immersive quality of AR itself, the dynamic nature of the animations, the explicit macro-micro linkage, or some synergy among all three. The researchers suggest that future work should separate the contribution of augmented reality from that of general visual support, a step that would sharpen the design principles for technology-based learning materials. They also note that the study involved a small number of intact classes, a common constraint in school-based research, and that the findings are associative rather than proof of a universal causal mechanism.

Even with those caveats, the study lands at a moment when schools worldwide are weighing whether emerging technologies justify their cost and complexity in the classroom. Augmented reality in education has generated enthusiasm for years, but rigorous, controlled comparisons with well-matched non-AR alternatives remain comparatively rare. This study contributes something valuable to that conversation: evidence that visualization technology may matter most not at the beginning of conceptual change, where good text design already does much of the work, but at the end, where the hardest cognitive step, full model replacement, takes place. For physics educators wrestling with topics like electrostatics, where the relevant entities are invisible and the intuitive models are stubborn, that is a practically useful insight. It suggests that the question is not simply whether to use augmented reality, but where in the learning sequence it earns its place, and what representational structure it should carry when it does.

Subject of Research: Augmented reality-assisted refutational texts with a macro-micro model to support secondary students’ mental models

Article Title: Augmented reality-assisted refutational texts with a macro-micro model to support secondary students’ mental models

Article References: Augmented reality-assisted refutational texts with a macro-micro model to support secondary students’ mental models. (n.d.). https://doi.org/10.1007/s43545-026-01788-9

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01788-9

Keywords: Augmented, reality-assisted, refutational, texts, macro-micro, model, support, secondary, students, mental, models, scientific research

The study, published in Global Surgical Education, the Journal of the Association for Surgical Education, emerged from a problem that will feel familiar to clinicians everywhere. Secure messaging embedded within the electronic medical record has become the standard channel for continuous communication among multidisciplinary teams, replacing pagers and phone calls with a searchable, auditable, and legally compliant record. Yet the same features that make these platforms attractive, their immediacy and their low barrier to sending a message, also make them prone to overuse. At the trauma center in question, investigators identified high secure chat volume and fragmented communication as concrete barriers to efficiency, resident education, and interprofessional collaboration between surgeons and nurses.

The consequences of message overload are not merely anecdotal. A growing body of research has linked electronic health record stress to clinician burnout, and studies of clinical decision support systems have documented how repeated alerts produce a phenomenon known as alert fatigue, in which users become desensitized to notifications and may begin ignoring them. Direct observation studies in emergency departments have shown that interruptions and multitasking are associated with task errors among physicians, while prospective observational research has tied workflow interruptions to increased workload for hospital doctors. For surgical residents, whose training depends on sustained engagement in the operating room and at the bedside, the stakes are particularly high: every ping during an operation or a teaching moment represents a potential disruption of intraoperative learning.

To understand the scale of the problem, the research team, led by Jackson A. Fos of the University of Tennessee Health Science Center College of Medicine in Chattanooga, together with colleagues at Erlanger Health, began with measurement rather than assumption. Baseline data were extracted from the electronic medical record’s secure chat logs over two separate 72-hour audit periods, providing a quantitative portrait of who was messaging whom, and how often, across the trauma surgery service. This audit-driven approach is a hallmark of rigorous quality improvement methodology: rather than relying on impressions of being overwhelmed, the team established an objective numerical baseline against which any intervention could be judged.

The intervention itself was designed by a multidisciplinary quality improvement team that included resident surgeons, advanced practice providers, and nursing leadership, ensuring that the people affected by the workflow shaped its redesign. The core insight was one of triage and routing: not every message directed at a resident needed to reach a resident. During daytime hours, defined as 06:00 to 16:00, nursing communications were rerouted to advanced practice providers rather than to the resident surgeons. The APPs, who are experienced clinicians embedded in the trauma service, would address messages falling within their scope of practice directly, and escalate only unresolved or genuinely resident-level concerns to the appropriate provider. This created a filtering layer that preserved the clinical content of communication while shielding trainees from routine traffic.

Rerouting messages was only one strand of a broader communication restructuring. The intervention package also included structured handoffs, which standardize the transfer of patient information between shifts and reduce the need for clarifying messages later in the day. Multidisciplinary rounds were enhanced, bringing nurses, APPs, and residents into the same physical conversation at the bedside so that questions could be answered synchronously rather than through asynchronous chat threads. Finally, the team delivered education on communication expectations, establishing shared norms about what belongs in a secure message, who should receive it, and when an alternative channel is more appropriate. A three-month implementation period was deliberately established before post-intervention audits, allowing the new workflow to bed in and avoiding the trap of measuring a system mid-transition.

The results, measured against the baseline audits, were substantial. Residents demonstrated a 27.7 percent reduction in the average number of messages received per 24-hour period. Nursing-related communications to residents fell by 35.9 percent, and during the targeted daytime window of 06:00 to 16:00, the nursing communications received by residents dropped by 31.3 percent. The authors attribute this daytime improvement to the APP management of routine concerns, enhanced communication during rounds, and improved utilization of the secure messaging platform itself. In other words, the intervention did not simply shift burden silently onto someone else’s shoulders; it changed how the whole team communicated, making the messaging system more purposeful for everyone involved.

What makes these findings resonate beyond a single trauma service is the way they reframe the problem of communication fatigue. Much of the discourse around electronic health records treats message volume as a byproduct of software design, something to be solved by vendors building better interfaces. This study suggests that a significant portion of the burden is organizational rather than technological. The same platform, with the same features, produced dramatically different message loads once the team clarified roles and routing. The intervention required no new software, no capital expenditure, and no change to the medical record itself, only a deliberate redistribution of communication responsibilities that leveraged the existing expertise of advanced practice providers.

The educational implications deserve particular attention. Surgical residency is built on apprenticeship, and the operating room is its most protected classroom. When residents are tethered to a buzzing device, answering routine questions about laboratory values or discharge logistics, the threads of intraoperative teaching unravel. By reducing message volume by more than a quarter, the workflow change returned a measurable slice of cognitive bandwidth to trainees during precisely the hours when most elective and trauma operations take place. The authors emphasize that these findings highlight the impact of communication restructuring on the resident workflow and the educational environment within academic surgical services, positioning message management as a matter of curriculum protection, not just administrative convenience.

There are also lessons here for interprofessional collaboration, an area where poorly designed communication systems can quietly breed friction. Nurses need timely responses to patient concerns; residents need uninterrupted focus; APPs occupy a clinical middle ground that is often underused as a communication hub. By formally recognizing APPs as the first point of contact for daytime nursing communications, the trauma center did not merely offload work, it clarified a professional role and, according to the study’s acknowledgements, the APPs’ willingness to assume an expanded role in frontline communication management was essential to the project’s success. The result was a system in which each message reached the person best positioned to act on it, the definition of communication efficiency in a complex clinical environment.

For hospital leaders watching message volumes climb on their own services, the study offers a replicable template: audit the actual traffic, convene a multidisciplinary team to identify workflow inefficiencies, reroute routine communications to appropriate intermediaries, standardize handoffs and rounds, set explicit communication expectations, and then wait through a genuine implementation period before measuring again. The 27.7 percent overall reduction and the 35.9 percent drop in nursing-related messages achieved in Chattanooga demonstrate that meaningful relief from message fatigue is achievable with modest, well-targeted changes. As secure messaging continues its spread through healthcare, the question is shifting from whether to use these platforms to how to use them well, and this study provides one of the clearest answers yet: less ping, more purpose.

Subject of Research: Reducing secure messaging burden and communication fatigue among surgical residents through workflow restructuring

Article Title: Less ping, more purpose: reducing message fatigue and boosting collaboration through secure chat optimization

Article References: Fos, J. A., Jones, J. M., Mezick, H. M., Rippy, M. G., Nunez, N., Buerster, K. B., Zeringue, A., Holder, L., Pairitz, M., Urevick, A., Cox, E. S., Spain, S., Holladay, J., Giles, W. H., & Bhattacharya, S. D. (2026). Less ping, more purpose: reducing message fatigue and boosting collaboration through secure chat optimization. Global Surgical Education – Journal of the Association for Surgical Education, 5(1), Article 187. https://doi.org/10.1007/s44186-026-00603-6

Image Credits: AI Generated

DOI: 10.1007/s44186-026-00603-6

Keywords: secure messaging, communication fatigue, surgical residency, electronic medical record, advanced practice providers, quality improvement, trauma surgery, interprofessional communication, resident education, workflow optimization, alert fatigue, multidisciplinary rounds

The urgency behind the effort is rooted in one of the defining demographic shifts of the twenty-first century. The United Nations projects that by 2050 nearly 68 percent of the world’s population will live in urban areas, with the fastest growth concentrated in South Asia and Africa. In low- and middle-income countries, unplanned urban expansion routinely outpaces infrastructure, producing informal settlements with unsafe housing, poor sanitation, unreliable water, and exposure to flooding, waste pollution, and contaminated air. These clustered environmental and social risks drive diarrheal disease, mosquito-borne infections, and respiratory illness. Yet the people most exposed, including migrants, the urban poor, women, and ethnic minorities, are often invisible in official records, which means municipal planners cannot target resources to the neighborhoods that need them most.

The technical problem the researchers set out to solve is fragmentation. In many cities, health, education, water, civil registration, and planning data sit in separate departmental databases with no interoperability, inconsistent coding, and incompatible geographic coverage. Routine health information systems such as clinic records and vital registration are typically designed for national or rural contexts and rarely capture the plurality of providers in urban areas or the granularity needed at ward level. Periodic household surveys offer snapshots but are infrequent and often miss slum settlements entirely. The result is that a slum cluster with high child mortality or no safe water can remain hidden inside favorable city-wide averages, and planning an immunization campaign or a new clinic becomes an exercise in guesswork.

The study will proceed in three sequential phases. From March to November 2025, the team will conduct formative research, including stakeholder interviews and a policy review, alongside a household census and social mapping. From November 2025 to June 2026, they will develop the portal itself, integrating, validating, and visualizing the data. From July to December 2026, the focus shifts to implementation, user engagement, capacity strengthening, and embedding the portal into municipal decision-making. Budhanilkantha was purposively selected because its diverse and rapidly changing population, including dense informal settlements and migratory groups, makes it an ideal proving ground, while remaining manageable for intensive fieldwork.

The formative phase begins with approximately twelve key informant interviews with municipal officials from departments spanning health, education, civil registration, disaster management, animal health, agriculture, environment, infrastructure, information technology, and women, children and inclusion. Semi-structured interviews conducted in Nepali will explore existing data sources and workflows, priority health issues, and barriers to data sharing. Transcripts will be analyzed thematically using NVivo, with two researchers independently coding an initial subset and resolving discrepancies through discussion. Alongside the interviews, the team will review municipal plans, health policies, and census reports, and compile an inventory of routine data sources, including the national Health Management Information System, civil registration records, and education databases, as well as non-routine sources such as past surveys and geographic information system layers.

Three interlinked primary data collection activities will then fill the gaps that routine systems leave. First, GIS-based social mapping will engage community members, youth volunteers, and ward officials in delineating ward boundaries and marking health providers, water sources, schools, markets, transport routes, garbage dumps, and flood zones, along with informal settlements and even shops selling meat, tobacco, and liquor, all of which are relevant to environmental and behavioral health risks. Second, a door-to-door household census will enumerate every household in Wards 4 and 7, capturing household composition, assets, mortality, healthcare access, risk behaviors, maternal and child health, immunization, and HPV vaccination coverage among adolescent girls. Anthropometric measurements, including height, weight, body mass index, blood pressure, and mid-upper arm circumference in children under five, will be recorded, with enumerators using tablets running electronic forms such as Open Data Kit for daily uploads to a secure database.

Third, the team will assess all eleven public health facilities serving the two wards, including one public hospital, six health posts, and four basic health service centers. Structured tools will capture infrastructure, service readiness, equipment, logistics, the availability of eighteen tracer drugs, and the qualifications and training of health care providers. Performance will be benchmarked against Nepal’s Minimum Service Standards, a nationally endorsed framework of the Ministry of Health and Population. GPS coordinates of each facility will enable spatial analysis, and facility data will be linked with household census data to reveal mismatches between where people live, what they need, and what services actually exist. Preliminary findings already illustrate the value of this approach: Ward 7 hosts a high density of informal settlers but is served by only one under-resourced clinic.

The technical architecture of the portal rests on common identifiers and geocoding. Each household receives a unique ID, with individual-level IDs nested within it, allowing records to be linked across datasets where consent permits, or linked spatially through shared geographic identifiers where it does not. Routine government data will be standardized and aligned with ward boundaries using a comprehensive data dictionary, and automated pipelines through APIs or periodic batch uploads will refresh the portal monthly or quarterly. Built on the open-source DHIS2 platform enhanced with GIS modules, the portal will feature interactive dashboards, mapping layers for household density, infrastructure, water and sanitation coverage, and health outcomes, hotspot analytics, custom report generation, and role-based access controls to protect privacy. A user-centered design approach, with participatory workshops, prototype testing, and feedback loops involving municipal officials and community representatives, is intended to ensure usability and long-term adoption.

Data security receives careful attention throughout. All datasets will be stored in password-protected systems with access restricted to a designated municipal focal person and authorized team members, with routine quality checks for missing, duplicate, and out-of-range values, and geospatial information that could identify individual households will never be publicly displayed at an identifiable level. Ethical approval was granted by the Nepal Health Research Council and the University of Leeds, and the study is part of the Community-led Responsive and Effective Urban Health System research consortium funded by UK aid through the Foreign, Commonwealth and Development Office.

The implications extend well beyond two wards in Kathmandu. Because the architecture is built on common identifiers and flexible GIS layers, the framework can be extended to additional wards and municipalities, with indicators tailored to local contexts such as air quality sensors or traffic accident hotspots. The researchers envision future integration of environmental sensors, urban mobility data, and predictive modeling for disease outbreaks, potentially evolving into a community-facing platform with public health alerts and citizen reporting. By demonstrating that participatory mapping, a household census, and facility surveys can be fused with routine administrative data into a living analytical tool, the project offers a proof of concept for equity-focused urban health governance, aligning local action with Sustainable Development Goal 11 on sustainable cities. For the millions of urban residents across low- and middle-income countries whose lives and needs are currently missing from the data, this Nepali pilot suggests that being counted may be the first step toward being served.

Subject of Research: Development of an integrated urban health data portal to strengthen municipal health systems in Nepal

Article Title: Study protocol for developing an integrated urban health data portal to strengthen urban health systems in Nepal

Article References: Kakchapati, S., Sharma, N., Baral, J., Maharjan, S., Mainali, S., Marasini, S., Elsey, H., Lamichhane, B., Karki, S., Bhattarai, A., Sharma, S., Shrestha, G., Karki, S., Ebenso, B., Hicks, J., Dawkins, B., Koirala, B., Dahal, K. P., Lama, A., & Baral, S. C. (2026). Study protocol for developing an integrated urban health data portal to strengthen urban health systems in Nepal. Discover Cities, 3(1), Article 200. https://doi.org/10.1007/s44327-026-00377-y

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00377-y

Keywords: urban health, data integration, Nepal, GIS, health informatics, household census, informal settlements, health equity, DHIS2, municipal governance, Kathmandu, study protocol

The platform, which the researchers call Herd-Immunity-on-a-Chip, or HIC, consists of 444 interconnected hexagonal microchambers arranged across a compact chip. Each chamber functions as a spatially organized social space, while the microchannels linking the chambers act as controllable routes of contact between them. In this miniature society, lung fibroblast cells play the role of individuals. Some of these cells were infected with coronavirus and placed at the epicenter of the network, while susceptible and non-susceptible cells were distributed across the surrounding chambers to represent people with differing levels of vulnerability. The design transforms what is normally a static cell-culture assay into a dynamic model of a population, complete with geography, connectivity, and heterogeneity.

The motivation for the work stems from a persistent limitation in infectious disease research. Classical epidemiological approaches, including the compartmental models that underpin much of public health planning, often assume that a population mixes uniformly, meaning that every individual has an equal chance of contacting every other. Real societies do not behave this way. People cluster in households, workplaces, and transit networks; density varies enormously between neighborhoods; and behavioral measures such as social distancing deliberately break the assumption of uniform contact. Mathematical models can incorporate such structure, but their conclusions depend on the assumptions fed into them. The SKKU team sought a system in which spatial structure, density, and mobility are not assumed but physically built into the experimental apparatus itself.

To construct that system, the researchers exploited the precision of microfluidic engineering. The hexagonal geometry of the chambers was chosen to create a densely connected lattice in which each chamber communicates with its neighbors through narrow channels, mimicking the way infection travels along routes of contact in a real community. By seeding infected cells at a defined location, the team created a controlled outbreak epicenter. Over the following seven days, they monitored viral transmission across the entire network in real time, watching as infection either propagated from chamber to chamber or stalled as it encountered barriers of reduced susceptibility or restricted movement. This longitudinal, spatially resolved observation is something that neither traditional cell assays nor population-level surveillance can easily provide.

The results revealed how profoundly population structure shapes the fate of an outbreak. When susceptible cells were densely packed within the chambers, or when the initial number of infected cells at the epicenter was high, frequent cell-to-cell contact allowed the virus to accelerate across the interconnected network. Dense, mobile, and largely susceptible populations, in other words, behaved exactly as epidemiologists would predict: they offered the virus abundant opportunities to spread. The chip made this abstract principle visible, showing transmission pathways lighting up across the lattice as the infection found routes through well-connected, vulnerable regions of the miniature society.

The most striking finding emerged when the researchers increased the proportion of non-susceptible cells in the population. When these resistant cells made up 80 percent or more of the total, the pathways available to the virus became fragmented, and viral spread was effectively suppressed across the chip. This reproduces, in a physical laboratory system, the phenomenon of herd immunity: the point at which enough individuals in a population are immune that transmission chains can no longer sustain themselves, indirectly protecting those who remain susceptible. The classical threshold for herd immunity depends on the basic reproduction number of a pathogen, and the 80 percent figure observed on the chip falls within the range that models have long suggested for respiratory viruses of moderate transmissibility. Here, however, the threshold was not calculated but directly observed.

The platform also allowed the team to experimentally recapitulate the effect of social distancing. By restricting the movement of cells within the network, the researchers slowed transmission, demonstrating that reduced mobility disrupts the contact routes on which viral propagation depends. This mirrors the logic behind the distancing measures adopted worldwide during recent pandemics, but in the chip the effect can be isolated, quantified, and repeated under controlled conditions. Because the chambers and channels are engineered features of the device, the researchers can systematically vary density, connectivity, and mobility in ways that would be impossible in a real population, offering a bridge between the abstraction of mathematical models and the messiness of actual epidemics.

Professor Sungsu Park emphasized the significance of the achievement, noting that this is the first study to directly recreate and experimentally validate viral transmission and herd immunity, phenomena that had previously been predicted mainly through mathematical modeling and epidemiological studies, on a laboratory chip. According to Park, the platform is expected to help predict the level of population protection required when new viral variants emerge, to inform the design of effective distancing strategies, and to enable rapid evaluation of therapeutic or antiviral interventions. In each of these applications, the chip offers something simulations cannot: a living biological system in which actual cells, actual viruses, and actual infection events unfold under precisely controlled spatial conditions.

The collaborative effort behind the study reflects the interdisciplinary nature of the work. Ph.D. student Jiande Zhang of SKKU, Dr. Narina Jung of the Korea Institute for Advanced Study, Dr. Min-Hyeok Kim of SKKU, and Researcher Wanyoung Lim of Samsung Electronics served as co-first authors, bringing together expertise in microfluidics, computational modeling, and biomedical engineering. Professors Sungsu Park and Byung Mook Weon served as corresponding authors. The combination of engineering precision and biological realism was essential: the chip had to be designed so that its geometry faithfully represented population structure while the cells within it retained the susceptibility and behavior needed for genuine viral infection to proceed.

The broader implications of Herd-Immunity-on-a-Chip extend well beyond the specific virus used in these experiments. As a general platform, the approach could be adapted to study how different pathogens move through structured populations, how variants with altered transmissibility change outbreak dynamics, and how interventions ranging from antiviral drugs to behavioral measures alter the course of transmission. In an era when new viral threats continue to emerge and public health decisions must often be made faster than real-world data can accumulate, a controllable physical model of epidemic spread offers a valuable middle ground between theory and reality. The SKKU team’s chip does not replace epidemiology, but it gives epidemiologists something they have never had before: a herd, small enough to fit on a fingertip, whose outbreaks can be watched, interrupted, and replayed at will.

Subject of Research: Microfluidic modeling of viral transmission and herd immunity in structured cell populations

Article Title: SKKU research team recreates viral transmission and herd immunity on a chip

Article References: SKKU research team recreates viral transmission and herd immunity on a chip. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: herd immunity, microfluidics, viral transmission, organ-on-a-chip, epidemiology, coronavirus, social distancing, population density, lung fibroblasts, Sungkyunkwan University, Advanced Science, infectious disease modeling

The study, published in SSM – Population Health by Yeongmi Jeong, Meghan M. Skira, and Robbee Wedow, focuses on two polygenic scores, which are numerical summaries of the many small genetic differences scattered across a person’s genome. The first, called the smoking initiation score, aggregates variants associated with whether a person ever becomes a regular smoker. The second, the cigarettes-per-day or CPD score, aggregates variants associated with how heavily someone smokes once they start. Both scores were built from genome-wide association studies, or GWAS, that scanned the genomes of more than a million people of European ancestry to find single-letter DNA differences, known as single nucleotide polymorphisms, that statistically track with smoking behavior. Each person’s score is calculated by adding up the number of copies of the risk-increasing variants they carry, with each variant weighted by the strength of its estimated association.

Neither score is a crystal ball. Out-of-sample predictions indicate that the initiation score explains only about 4 percent of the variation in whether people start smoking, and the CPD score explains about 4 percent of the variation in how much they smoke. Within the study sample itself, a one standard deviation increase in the initiation score raised the probability of ever smoking by roughly 9 percentage points, while a one standard deviation increase in the CPD score was associated with about two additional cigarettes smoked per day among smokers. These are modest effects, but they are large enough, in a sample of this size, to reveal patterns that would otherwise remain invisible.

To measure health, the researchers constructed a standardized index combining thirteen distinct outcomes: self-reported general health, hospitalization, body mass index, seven doctor-diagnosed chronic conditions including high blood pressure, diabetes, cancer, lung disease, heart problems, stroke, and arthritis, and limitations in basic and instrumental activities of daily living. The analysis covered 48,444 person-wave observations from 9,515 unique participants aged 50 to 65, drawn from survey waves between 1994 and 2018. The age range was chosen deliberately, to minimize survival bias, since people with the worst health may not survive to be surveyed at older ages, and to avoid conflating genetic associations with the health effects of retirement.

The central finding is stark. A one standard deviation increase in the CPD score was associated with a 0.043 standard deviation increase in the health index, where higher values indicate worse health. Adding detailed controls for smoking behavior, including current and lifetime smoking status, the exact number of cigarettes smoked per day, the maximum ever smoked, the age of starting and quitting, and teenage smoking, attenuated the association only modestly, to 0.035 standard deviations. For context, that residual association is roughly 38 percent of the average health gap between ever smokers and never smokers in the sample. In other words, even after accounting for everything the surveys record about smoking, the genetic score still carries substantial information about health.

The most provocative result emerged when the researchers restricted the analysis to never smokers, a group of more than 20,000 observations with no reported smoking behavior to explain away. Among these individuals, a one standard deviation increase in either the CPD score or the initiation score was associated with roughly a 0.03 standard deviation increase in the health index. The CPD score also tracked with higher body mass index and obesity among never smokers, with a one standard deviation increase associated with 0.645 units of additional BMI. Because these individuals never smoked, the association cannot be attributed to their own tobacco use, although it could still reflect secondhand smoke exposure, unmeasured smoking-related environments, or something else entirely.

One obvious candidate is the family environment. Because children inherit genes from their parents, a person’s smoking-related genetic score may be correlated with parental smoking, parental substance use, parental education, and childhood socioeconomic conditions, all of which shape later health. The researchers tested this by adding controls for whether parents smoked during the respondent’s childhood, whether parents drank or used drugs heavily enough to cause family problems, parental mortality timing, parental years of schooling, and self-reported childhood financial status. These factors accounted for a modest share of the association, but more than 75 percent of the CPD score’s relationship with health remained, and about 60 percent of the initiation score’s relationship among never smokers survived the most comprehensive specifications.

The researchers then turned to psychology. Prior work has shown that smokers tend to be more impatient, risk-tolerant, and impulsive than non-smokers, and the team asked whether people with higher smoking-related genetic scores differ along similar dimensions. They found little evidence of a link with measured risk preferences, but higher scores were significantly associated with shorter financial planning horizons, greater pessimism, and lower conscientiousness, traits that economists and psychologists have long connected to lower investment in health. When these behavioral and psychological characteristics were added to the models simultaneously, they explained at most 21 percent of the remaining association between the CPD score and health, and 18 percent of the initiation score’s association among never smokers. Even with the fullest set of controls, at least 69 percent of the CPD score’s association with health persisted.

The authors are careful to emphasize what the study does not show. Polygenic scores are not clean measures of direct genetic effects. The GWAS estimates underlying them can absorb indirect genetic effects, such as parental genotypes shaping the environments children experience, as well as confounding from assortative mating, population stratification, collider bias, and case-control ascertainment. There is also the problem of pleiotropy: the same genetic variants that predict smoking may influence other biological processes and traits that affect health directly. Genetic correlation analyses have indeed documented significant overlap between smoking behaviors and a wide range of health, behavioral, and socioeconomic traits. The associations reported here are descriptive, not causal, and the study’s restriction to individuals of European ancestry, a consequence of the limited predictive power of European-derived scores in other populations, means the findings may not generalize more broadly.

Even with those caveats, the implications are significant. Economists studying smoking have long suspected that people who smoke differ from those who do not in ways that extend beyond the behavior itself, and some modeling work has speculated that genetics or correlated risk-taking might explain why smokers face elevated chronic disease and mortality risks beyond what smoking alone explains. This study provides some of the most direct evidence yet that such unobserved heterogeneity is real and partially visible in the genome. Smoking-related polygenic scores, the results suggest, contain information about health that detailed behavioral histories do not capture, pointing toward shared genetic architecture linking tobacco use, personality, and long-run wellbeing. Untangling those threads, the authors argue, will be essential for interpreting genetic risk scores in health research, and for understanding why two people with identical smoking histories can end up with very different health outcomes decades later.

Subject of Research: Associations between smoking-related polygenic scores and health outcomes in midlife

Article Title: Exploring Links Between Health and the Genetic Risk for Smoking

Article References: Jeong, Y., Skira, M. M., & Wedow, R. (2026). Exploring Links Between Health and the Genetic Risk for Smoking. SSM – Population Health, Article 101982. https://doi.org/10.1016/j.ssmph.2026.101982

Image Credits: AI Generated

DOI: 10.1016/j.ssmph.2026.101982

Keywords: polygenic scores, smoking initiation, smoking intensity, Health and Retirement Study, GWAS, pleiotropy, chronic disease, health behavior, genetic risk, conscientiousness, never smokers, population health

The research team, led by Jemima Jaman Celia and corresponding author Shah Md. Atiqul Haq from the Department of Sociology, framed the problem in explicitly psychosocial terms. Dental appearance, they note, may have a significant impact on individuals’ self-image and confidence when interacting with others in different social contexts. To capture that impact, the cross-sectional study assessed three dimensions related to dental appearance and social confidence: dissatisfaction with dental appearance, confidence while smiling or speaking, and confidence in social situations. Participants completed a structured questionnaire, and the researchers selected respondents through convenience sampling, a design choice that the authors acknowledge shapes how broadly the results can be generalized beyond the Sylhet university population studied.

Methodologically, the study leaned on a layered statistical strategy. Descriptive statistics provided an initial portrait of the sample, Chi-square tests probed associations between categorical variables, and one-way analysis of variance, or ANOVA, compared mean differences across groups. The centerpiece, however, was multinomial logistic regression, a technique suited to outcomes with more than two ordered or unordered categories. By adjusting for multiple variables simultaneously, the model produced adjusted odds ratios, abbreviated AORs, with 95 percent confidence intervals, allowing the team to estimate how strongly each factor predicted different levels of feeling bad about dental appearance or of confidence, while holding other influences constant. Statistical significance was set at p less than 0.05, the conventional threshold in health and social science research.

The adjusted regression results reveal a striking pattern. Students in STEM fields were more likely to report moderate levels of feeling bad about their dental appearance, with an adjusted odds ratio of 2.189 and a p-value of 0.032. Gum health emerged as a powerful driver: very poor gum health was associated with high levels of feeling bad about dental appearance, with an AOR of 2.765 and a p-value of 0.008, while fair gum health carried an AOR of 1.905 with a p-value of 0.007. In other words, the worse students rated their gums, the more likely they were to experience strong negative feelings about how their teeth looked, an association that persisted even after accounting for other variables in the model.

Perhaps counterintuitively, several factors the researchers examined were associated with lower odds of high distress. Reporting no tooth discoloration, no teeth misalignment, not considering orthodontic treatment, and disagreeing with social media comparison were all linked to high levels of feeling bad about dental appearance, with adjusted odds ratios ranging from 0.258 to 0.476. An odds ratio below one indicates a reduced likelihood relative to the reference category, so these findings suggest that students free of visible dental complaints and free of appearance-related social comparison were less likely to fall into the most distressed group. The consistency of the direction across these four variables lends weight to the interpretation that both objective-looking self-assessments and subjective social pressures matter.

Confidence-related outcomes told a complementary story. Tooth discoloration, consideration of orthodontic treatment, social media comparison, and concern about bad breath were all associated with the confidence measures, with adjusted odds ratios between 0.178 and 0.430. Concern about bad breath is particularly interesting because it is an oral condition that is largely invisible to the self but imagined to be highly salient to others. Its association with confidence while smiling or speaking, and with confidence in social situations, supports the study’s central thesis that social confidence is influenced not only by how students perceive their own dental appearance but also by how they believe others evaluate them. The gap between perceived self and perceived other, the authors suggest, is where much of the psychological burden seems to sit.

The ANOVA results sharpened the picture of which factors cut across all outcomes. Consideration of orthodontic treatment and social media comparison were associated with all four study outcomes, with p-values below 0.001, making them the most pervasive influences in the dataset. Orthodontic treatment consideration is a revealing variable: it captures students who are dissatisfied enough with alignment or appearance to contemplate braces or aligners, a decision that blends clinical need with aesthetic aspiration and, increasingly, with norms broadcast through digital platforms. Social media comparison, meanwhile, reflects the constant exposure to curated faces and smiles that characterizes contemporary online life, and its strong statistical footprint here echoes a growing international literature on appearance anxiety among young people.

Concern about bad breath, by contrast, was associated only with the confidence-related outcomes, with p-values below 0.01, and not with the broader measure of feeling bad about dental appearance. That dissociation is scientifically meaningful. It implies that different psychosocial factors operate on different channels: some, like gum health and misalignment, feed into a general negative self-appraisal of one’s teeth, while others, like breath concerns and discoloration, more directly undermine the willingness to smile, speak up, or engage in social settings. For clinicians and university counselors alike, the implication is that a single intervention aimed at dental aesthetics may not automatically resolve every confidence-related complaint, and vice versa.

The study was approved by the SUST Research Ethics Board under reference SREB/SSS/SOC/02 (2026), based on review by the School Ethics Review Committee of the School of Social Sciences. The researchers state that all data collection methods adhered to the ethical standards of the Declaration of Helsinki, including protections for participant anonymity, privacy and dignity. Consent procedures were conducted in Bengali, participants were informed they could withdraw at any time without penalty, and participants with low reading levels underwent verbal consent with witness confirmation. The authors declare no competing interests, and the study received no external funding, details that matter for readers weighing the independence of the findings.

The authors conclude that addressing both psychosocial factors and perceived oral conditions may help improve the social well-being of students. That dual prescription is the study’s most actionable takeaway. Universities in Bangladesh and beyond could pair accessible dental services with counseling that addresses appearance-based anxiety, particularly the kind amplified by social media comparison. At the same time, the convenience sampling design and the single-city setting mean the results should be read as a well-grounded signal rather than a definitive national estimate. Even so, for the 534 students surveyed, the message is clear: the state of one’s gums, the color of one’s teeth, and the silent comparisons made on a phone screen all converge on a single, socially consequential question of whether it feels safe to smile.

Subject of Research: Psychosocial factors linking dental appearance to social confidence among university students in Bangladesh

Article Title: Factors influencing social confidence in relation to dental appearance among university students in Bangladesh

Article References: Celia, J. J., Chowdhury, N., Jesun, M. A. K., Arafat, Y., & Haq, S. M. A. (2026). Factors influencing social confidence in relation to dental appearance among university students in Bangladesh. Discover Social Science and Health. https://doi.org/10.1007/s44155-026-00492-6

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00492-6

Keywords: dental appearance, social confidence, university students, Bangladesh, oral health, psychosocial factors, orthodontic treatment, social media comparison, gum health, tooth discoloration, bad breath, multinomial logistic regression

The research team, led by Diego Benito, Jose Aguilar, Juan Marcos Ramírez and Antonio Fernández Anta, set out to answer a deceptively simple question. If democracy indices are supposed to capture the quality of democratic life in a country, how much of that quality is actually encoded in the broader social and economic conditions in which democracies operate? Rather than running a handful of regressions, the group constructed classification models for three distinct democracy indices using multiple machine learning techniques and three different dataset configurations: one containing only political variables, one combining political and context variables, and one built exclusively from context variables such as education, health and social development indicators drawn from open repositories including the V-Dem dataset, Our World in Data and World Bank Open Data.

The methodological design is where the study distinguishes itself. The authors trained classifiers on each configuration and then subjected them to a battery of cutting-edge explainability methods: Local Interpretable Model-Agnostic Explanations (LIME), which probes individual predictions by perturbing inputs; SHapley Additive exPlanations (SHAP), which distributes credit for each prediction among features using game-theoretic principles; and Feature Importance Based on Random Values (FIRV), which benchmarks feature scores against randomly generated variables to filter out spurious attributions. Crucially, the team added a fourth layer of validation using the RemOve And Retrain (ROAR) technique, in which the supposedly most important features are removed and the model is retrained from scratch. If performance collapses, the explanation was real; if it barely changes, the explanation was likely noise. This combination of explanation and adversarial validation is still rare in social science applications of machine learning.

The results were consistent across all three democracy indices studied. Adding context variables to the political ones consistently improved or at least matched classification performance, meaning the models could distinguish more democratic from less democratic countries more reliably when socioeconomic information was included. More provocatively, models trained on context variables alone achieved predictive performance comparable to — and in some cases superior to — models built exclusively from the political variables that traditionally define these indices. In practical terms, a machine learning model that never sees a single measure of electoral freedom or party competition can still classify countries on a democracy index with remarkable accuracy, simply by examining patterns in education, health, energy and development data.

The explainability analyses then went a step further, identifying which specific context variables carried the most weight. Among the most influential was renewable energy consumption, which emerged as a highly important feature for both the Electoral Democracy Index and the Political Corruption Index. That finding is likely to raise eyebrows in both political science and energy policy circles: it suggests a measurable statistical association between a country’s transition toward renewable energy and its standing on indices of democratic quality and corruption, independent of the political variables usually used to explain those standings. The authors are careful to frame such relationships as insights into the structure of the data rather than proof of causation, but the associations offer new hypotheses about how socioeconomic conditions and democratic quality intertwine.

The study builds on a growing but still contested literature. Previous work had used machine learning to construct new democracy measures, including datasets covering 186 countries from 1919 onward, and a substantial body of research has explored links between democracy and outcomes ranging from economic growth and health to emissions, energy efficiency, public media funding and transparent lobbying. What most of that work shared was a framing in which democracy was the explanatory variable and social outcomes were the consequences. The new analysis inverts the perspective, asking instead whether the social and economic context can reconstruct the democracy measurement itself — and finding that it largely can.

Technically, the researchers faced the standard obstacles of cross-national social data: missing values, class imbalance and the risk of overfitting. Their pipeline drew on established remedies, including synthetic minority over-sampling (SMOTE) to balance classification categories, hyperparameter optimization with the Optuna framework, and careful treatment of missing data following best practices in statistical analysis. Variance inflation factors were used to guard against multicollinearity among features. By applying the same disciplined pipeline across three different indices and three dataset configurations, the team could check whether any single result was an artifact of one index’s particular construction — and the consistency of the findings across indices is one of the study’s strongest points.

The implications extend beyond academic measurement. Democracy indices are not just descriptive instruments; they inform country risk assessments, development aid allocations, governance conditionality and countless comparative studies. If a substantial share of the information in those indices is statistically recoverable from socioeconomic context, then index designers may need to think harder about what their measures actually capture and whether political and social dimensions are being conflated. The authors argue that their findings provide a basis for new data-driven approaches to understanding and strengthening democracy, and specifically for incorporating relevant context variables into the development of future democracy indices and, more importantly, into the design of evidence-based public policies.

There are, of course, limits to what classification models can establish. Machine learning identifies predictive structure, not causal mechanisms, and the study itself does not claim that investing in renewable energy or education will mechanically raise a country’s democracy score. The direction of causality between social development and democratic quality has been debated for generations, and the honest reading of this work is that the two are so deeply entangled that political variables alone were never going to tell the full story. What the study does deliver is a transparent, validated demonstration of that entanglement, using explainability methods that allow other researchers to inspect exactly which features drive which predictions rather than accepting a black box.

For a field that has measured democracy almost exclusively through political lenses for the better part of a century, the message of this research is quietly radical: the context in which democracies exist is not background noise but signal. Education systems, health outcomes, energy choices and broader social development appear to encode enough information to reproduce the judgments of the world’s democracy indices — and sometimes to do so better than the political indicators themselves. As machine learning and explainability tools continue to mature, the study suggests that the next generation of democracy measurement may look less like a checklist of institutional features and more like a multidimensional portrait of the societies in which those institutions either flourish or fail. All data underlying the work comes from open repositories, which means other teams can immediately test, replicate and extend these findings — a fittingly democratic way to advance the science of measuring democracy itself.

Subject of Research: Machine learning analysis of how socioeconomic context variables influence democracy indices

Article Title: Analysis of the impact of context variables on democracy indices using machine learning approaches

Article References: Benito, D., Aguilar, J., Ramírez, J. M., & Anta, A. F. (2026). Analysis of the impact of context variables on democracy indices using machine learning approaches. SN Social Sciences, 6(10), Article 508. https://doi.org/10.1007/s43545-026-01802-0

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01802-0

Keywords: democracy indices, machine learning, explainability, SHAP, LIME, ROAR, political science, socioeconomic variables, renewable energy, classification models, V-Dem, public policy