The research team, led by Hande İleri of the University of Health Sciences Turkey, Izmir Faculty of Medicine, together with Muhammed Mustafa Uzan and Yasemin Kılıç Öztürk, drew on the 2023 Türkiye Older Persons Profile Survey, a nationally representative dataset collected by the Turkish Statistical Institute. Because the analysis used anonymized secondary microdata obtained through official permission procedures, the study did not require separate ethics committee approval, and consent was handled as part of the original survey protocols. What makes the study methodologically notable is its scale and its weighting approach: rather than a convenience sample of clinic patients, it captures the older population of an entire country, with survey weights applied so the results reflect the true demographic composition of Türkiye’s aging citizens.

To measure digital engagement with health systems, the researchers constructed a composite score built from four components: mobile internet use, general internet browsing, use of mobile health applications, and use of e-government services. This composite approach matters because digital access is not a single on-or-off switch. An older adult might own a smartphone but never open a health app; another might browse the web fluently yet be stymied by the authentication steps of a government portal. By combining these indicators, the score captures a spectrum of practical, health-relevant digital capability rather than mere device ownership. On the other side of the analysis, healthcare access difficulty was measured with a composite score based on seven distinct reported problems in accessing or using healthcare services, ranging from the struggle to secure appointments to broader obstacles in navigating the system.

The statistical engine of the study was a set of survey-weighted general linear models, a technique appropriate for complex survey data because it incorporates the sampling weights of individual respondents, ensuring that underrepresented groups exert their correct influence on the estimates. After full adjustment for sociodemographic and health-related factors, the association held firm: each increment in the health-oriented digital access score was associated with a lower healthcare access difficulty score, with a coefficient of -0.045 and a 95 percent confidence interval spanning -0.066 to -0.024, and a p-value below 0.001. In plain terms, even after accounting for income, education, health status, and other confounders, older adults who were more digitally connected reported systematically fewer barriers to care.

Correlation, of course, is not causation, and the authors are careful to frame their findings as an association from cross-sectional data. It is possible that older adults who face fewer healthcare obstacles are also more likely to engage with digital tools, rather than the reverse. Yet the biological and social plausibility of the digital pathway is compelling. When appointment booking migrates to apps, when prescription renewals require portal logins, and when health information is distributed through websites, the person without those tools faces a compounding series of friction points. The finding that difficulty obtaining appointments affected fully half of the older population suggests that the scheduling bottleneck, increasingly digitized in many health systems, sits at the heart of the access problem.

The demographic backdrop amplifies the stakes. The proportion of older adults is rising worldwide, and Türkiye is squarely within this global transition. Healthy aging frameworks, including those promoted by international health bodies, emphasize timely access to healthcare as a pillar of wellbeing in later life. If the infrastructure of access is quietly shifting online while a large share of the elderly population remains offline, the digital divide ceases to be a technology story and becomes a health equity story. The Turkish data make this concrete: roughly one in seven older adults used mobile health applications, meaning that the vast majority, more than 85 percent, did not, even as digital channels became more common at many stages of healthcare access.

The study’s authors argue that digital inclusion should be treated as an integral component of broader efforts to promote equitable healthcare access for older adults, not as a separate technology policy. That framing has practical implications. Digital literacy programs targeted at seniors, simplified authentication for e-government health services, and hybrid systems that preserve telephone and in-person booking channels could all narrow the gap. The composite score developed in the study also offers a template for other countries: by measuring mobile internet use, browsing, health app adoption, and e-government engagement together, health ministries can track whether their digital transformation is actually reaching the populations that need care most.

Türkiye’s situation resonates far beyond its borders. Many health systems, from Europe to East Asia, have accelerated digitization in the wake of the COVID-19 pandemic, moving vaccination records, telemedicine, and appointment systems onto smartphones. Older adults everywhere show lower rates of adoption of these tools, and the Turkish study provides one of the clearest population-based quantifications of how that gap maps onto real difficulties in obtaining care. The finding that e-government use, often the gateway to subsidized or state-coordinated health services, stood at just 14.5 percent among older Turks illustrates how quickly administrative modernization can outpace the populations it is meant to serve.

There are also technical lessons in how the study was conducted. Using the individuals sampling weight in the general linear models guards against the distortion that arises when survey designs oversample certain groups, and the composite scoring strategy avoids the fragility of single-item measures, which can conflate, say, owning a tablet with being able to complete a telemedicine consultation. The seven-item healthcare access difficulty score similarly acknowledges that barriers to care are multidimensional, encompassing cost, transport, information, and scheduling. Together, these methodological choices lend weight to the central conclusion that the digital access association is not an artifact of a poorly specified model.

The study received no specific grant funding from public, commercial, or not-for-profit agencies, and the authors declare no competing interests. Published open access under a Creative Commons license, the research invites replication in other national contexts, and its message is likely to travel well: as health systems digitize, they must carry their oldest users with them, or the promise of technology-enabled care will harden into a new form of exclusion. With 8.6 million older adults represented in a single national dataset, and half of them struggling simply to book an appointment, the numbers from Türkiye read as a quantified call to action for every aging society watching its healthcare front door move onto a screen.

Subject of Research: The association between health-oriented digital access and healthcare access difficulties among older adults in Türkiye

Article Title: Health-oriented digital access and healthcare access difficulties among older adults in Türkiye: a population-based cross-sectional study

Article References: Health-oriented digital access and healthcare access difficulties among older adults in Türkiye: a population-based cross-sectional study. (n.d.). https://doi.org/10.1186/s12889-026-29258-0

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29258-0

Keywords: digital health, older adults, healthcare access, Türkiye, e-government, mobile health applications, health equity, aging, digital divide, BMC Public Health, cross-sectional study, health services accessibility

The study, conducted by Zander Weisman Mintz of the LBJ School of Public Affairs at the University of Texas at Austin and Ji Ma, now of Indiana University Bloomington and the University of Oxford, combines a large-scale systematic literature review with cutting-edge computational text analysis. Drawing on OpenAlex, an open scholarly database, the researchers built a corpus through citation snowballing that began with four foundational review articles and expanded forward through more than 14,900 candidate works. They then screened this mountain of material using Meta’s open-source Llama3 large language model, employing role prompting and chain-of-thought prompting to classify each candidate against six qualifying conditions. Validation against a manually reviewed sample of 200 works achieved 91 percent agreement. The final corpus comprised 3,268 works, published between the mid-1980s and May 2026.

To extract the latent thematic structure of this literature, the team applied BERTopic, a neural topic-modeling technique that leverages transformer-based document embeddings and clusters abstracts into coherent topics represented through class-based term frequency–inverse document frequency. The method outperformed conventional bag-of-words alternatives such as latent Dirichlet allocation and non-negative matrix factorization, particularly on the short texts typical of academic abstracts. Hyperparameters were optimized through random grid search to maximize topic coherence, and outlier reclassification plus manual relabeling refined interpretability. The result was a map of twenty distinct topics spanning the entire field, from healthcare AI and municipal waste optimization to algorithmic fairness and citizen participation in datafied democracies.

But topic modeling alone would simply produce another taxonomy. The study’s central contribution is a functional framework that organizes the literature around four practical functions of public governance: creating public value, delivering public services, responsiveness to the public, and protecting state–society relations. The authors deliberately rejected more familiar organizing axes. Sorting by application domain scatters shared governance problems like bias and accountability across every sector. Sorting by technology tracks capability but ignores the public stakes that make government deployment distinctive. Sorting by governance risk collapses the field into its pathologies. And sorting by public value alone absorbs distinctions a public manager most needs to see. The four governance functions, the authors argue, are the questions a public manager actually asks before deploying a system: does it create value, improve a service, change how the organization hears from citizens, and what does it do to the state’s relationship with the governed?

The framework’s dimensions are enriched by four theoretical lenses that recur throughout the scholarship: technological operationalization, which asks how AI fits specific public problems; function of government, which maps AI techniques onto sectoral activities; the policy-cycle framework, which locates AI within agenda-setting, formulation, implementation, and evaluation; and public governance, which foregrounds who is affected. Notably, the authors show that technological operationalization is not confined to the application dimensions but runs vertically through all four: a model’s opacity, its optimization target, and its hardcoded thresholds are design choices that propagate into accountability deficits and entrenched inequality downstream. Governance failures, they suggest, are often most cheaply addressed upstream at the point of technical design rather than through oversight bolted on after deployment.

The empirical findings reveal a field in dramatic flux. Output grew rapidly through the late 2010s, peaked at 627 works in 2023, and remained elevated through 2025. More striking is the compositional shift. The public value dimension, dominated by domain applications such as energy-efficiency forecasting and air-pollution monitoring, accounted for 50 to 77 percent of works through 2017 but declined to roughly 16 to 22 percent by 2024 and 2025. The state–society relations dimension, encompassing algorithmic fairness, ethics, and regulation, more than doubled its share, from around 12 to 23 percent in the mid-2010s to 52 to 54 percent in 2024 and 2025. The transition concentrates around the public release of ChatGPT in November 2022, which the authors use as a heuristic marker rather than a causal explanation, noting that generative AI diffusion, regulatory momentum such as the EU AI Act, and an ongoing accountability debate all converged in the same period.

The post-2022 pivot is stark in the numbers. Classical domain-application topics shrank from about 19 percent of the pre-ChatGPT corpus to under 5 percent afterward, while several topics essentially became new literatures: ethics and equity in educational AI grew from 2 works to 66, and AI ethics, accountability, and transparency in public service delivery surged from 9 to 291 works, a thirty-one-fold expansion. Semantic analysis of the twenty topics shows that the six state–society topics form a tight, mutually similar cluster, reading as a single coherent research conversation despite spanning multiple domains, while the public value topics spread widely across incompatible technical vocabularies. The framework thus partitions the field asymmetrically, and the authors argue this asymmetry is itself informative for anyone navigating it.

Equally important is where the literature is thin. The responsiveness dimension, concerning where AI can support the policy cycle and democratic feedback, carries only about 462 works across just two topics, making it the smallest dimension despite AI’s obvious relevance to agenda-setting, consultation, and evaluation. Intriguingly, public administration journals are already over-indexing here: responsiveness accounts for about a quarter of works in top-ranked public administration outlets, nearly double its corpus-wide share. The authors sketch four research directions for this space, including how large language models reshaping the information environment of agenda-setting, whether AI-mediated consultation strengthens or hollows the link between citizen voice and government decision, what happens when optimizing tools are embedded in satisficing policymaking processes, and whether AI-driven evaluation genuinely closes the feedback loop or merely produces dashboards.

The framework also doubles as a practical diagnostic for public managers weighing a specific AI system. Each dimension supplies a question and a concrete check: whether the system creates value the public can recognize, whether it improves a service without eroding recourse to a human, whether it widens or narrows the channel through which citizen preferences enter decisions, and whether affected citizens can learn of, understand, and contest its output while responsibility remains attributable. Applied retrospectively to the City of Chicago’s 2016 predictive policing heat list, a system later judged less effective than a simple most-wanted list, the diagnostic surfaces failures on three of four questions, illustrating how design-stage deficits that later evaluations documented could have been identified before deployment.

The study acknowledges its boundaries, including reliance on abstracts rather than full texts, the undercounting of grey literature and government reports, restriction to English-language scholarship, and the possibility of calibration drift in large-language-model screening. Yet within those limits, the contribution is a translation layer: a compact, function-based vocabulary that makes an interdisciplinary literature, previously scattered across engineering, information systems, and public administration, discoverable and comparable across the communities that study it in parallel. As governments worldwide grapple with deploying AI under democratic constraints, the researchers offer not a new theory of public administration but something arguably more useful right now: a shared map of what is known, what is contested, and where the next decade of scholarship and practice most urgently needs to go.

Subject of Research: A computational mapping of artificial intelligence research in the public sector using a functional governance framework and BERTopic modeling

Article Title: Towards artificial intelligence for the public sector: framing and bridging academia and practice

Article References: Mintz, Z. W., & Ma, J. (2026). Towards artificial intelligence for the public sector: framing and bridging academia and practice. Global Public Policy and Governance, 6(2), 124-156. https://doi.org/10.1007/s43508-026-00148-w

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00148-w

Keywords: artificial intelligence, public sector, public governance, algorithmic fairness, BERTopic, systematic literature review, policy cycle, public value, ChatGPT, accountability, e-government, computational social science

Researchers Cheche Duan and Shuo Chen of Shenzhen University, together with Zemin Jia of South China University of Technology, assembled an extraordinary dataset drawn from the government-citizen interaction portals of Shenzhen’s municipal and district governments, covering the years 2016 through 2024. These portals allow residents to submit requests ranging from complaints about neighborhood services to questions about policy procedures, and the government’s written replies are published online. Because the platforms record the full text of both requests and responses, the researchers could measure not just how quickly officials answered, but how well they answered, using computational text analysis to score the substance, politeness, and attentiveness of every reply.

The methodological design is central to the study’s credibility. Rather than simply comparing periods before and after the AI platform’s introduction, which risks confounding the technology’s effects with broader social and administrative trends, the team combined three complementary techniques. Regression discontinuity in time exploits the sharp cutoff of the platform’s launch date, comparing observations immediately before and after the transition as if they were randomly assigned. The event-study method then traces how any detected effects evolve month by month, revealing temporal dynamics that a single before-and-after comparison would conceal. Natural language processing supplied the fine-grained outcome measures, classifying response quality, efficiency, and attitude across the vast corpus of bureaucratic text.

The headline result is captured in the study’s own framing: lagged short-run efficacy followed by sustained long-run inefficacy. In practical terms, the AI-powered platform did significantly improve the quality of responses written by what the authors call screen-level bureaucrats, the officials who interact with citizens through digital interfaces rather than in person. But that improvement did not appear immediately. Instead, it emerged with a substantial temporal lag, suggesting that organizations needed time to absorb the new technology, adjust workflows, and translate algorithmic oversight into better written communication with the public.

Even more striking is the long-run pattern. The gains in response quality, once achieved, were constrained by organizational inertia and did not translate into sustained improvements across all dimensions of responsiveness. The researchers found that the top-down coercive institutional pressures generated by the AI platform, in which superiors can monitor and evaluate subordinates’ replies in real time, produced no significant gains in overall response efficiency or in the warmth and courtesy of officials’ attitudes. The technology sharpened the content of answers in some contexts but failed to make government faster or friendlier on average.

The effects were also selective in ways that illuminate bureaucratic incentives. Response quality improved most clearly for consultation requests, where citizens ask for guidance on policies and procedures, and at the district level of administration rather than the municipal level. The authors interpret this selectivity through the lens of institutional theory: when pressure flows from superiors through an AI-enabled monitoring platform, subordinates respond strategically, investing effort where scrutiny is most visible or where compliance is easiest to demonstrate, rather than upgrading performance uniformly. This echoes a long line of scholarship on symbolic responsiveness, in which bureaucracies under observation produce displays of compliance that do not necessarily reflect deeper organizational change.

The concept of the screen-level bureaucrat anchors the study’s theoretical contribution. Where classic public administration theory distinguished street-level workers exercising discretion face to face from system-level bureaucracies governed by automated rules, the digital age has produced an intermediate figure: an official whose entire interaction with the citizen is mediated through a screen, shaped by platform dashboards, algorithmic triage, and performance metrics. Understanding how such workers respond to AI-driven oversight is increasingly urgent as governments worldwide adopt smart platforms, chatbots, and automated case-management systems. The Shenzhen evidence suggests that algorithmic surveillance can raise the informational content of replies, but that it cannot by itself overcome the entrenched routines, workload pressures, and incentive structures that govern bureaucratic behavior.

The temporal dynamics carry particular weight for policymakers tempted to treat technology procurement as governance reform. If the benefits of an AI platform take months or years to materialize, evaluations conducted too early will either miss real gains or, conversely, if improvements are shallow and non-durable, will overstate what the technology can deliver. The study’s event-study estimates show the improvement in response quality unfolding gradually after implementation, while measures of efficiency and attitude remain essentially flat throughout the observation window. That asymmetry, the authors argue, reflects organizational inertia: established procedures and habits resist rapid change even when a superior-led platform makes performance transparent and comparable across departments.

The implications extend well beyond Shenzhen. Cities and national governments across Asia, Europe, and the Americas are investing heavily in AI-enabled citizen portals on the premise that digitization will close the accountability gap between the state and the public. The Shenzhen results caution that a superior-led, AI-powered platform is a limited instrument on its own. Because coercive pressure from above improved quality only in narrow slices of the workload, the authors conclude that sustainable administrative responsiveness requires strengthening the operational capacity of the frontline departments that handle public requests directly, and enhancing social self-governance so that citizens and communities share the work of articulating and resolving problems. Technology, in this account, is a magnifier of institutional conditions rather than a substitute for them.

The study also demonstrates a methodological template likely to spread through the field of digital governance research. By pairing quasi-experimental causal designs with natural language processing, the researchers converted hundreds of thousands of unstructured bureaucratic texts into measurable outcomes, capturing dimensions of responsiveness, such as politeness, substantive attentiveness, and procedural guidance, that traditional surveys or response-time metrics cannot reach. As large language models make such text analysis cheaper and more accurate, similar evaluations of government AI systems will become feasible elsewhere, allowing policymakers to test, rather than assume, whether their digital platforms deliver. For now, the Shenzhen evidence delivers a sober message: artificial intelligence can nudge bureaucrats toward better answers, but only slowly, only partially, and never without the organizational foundations that make responsiveness durable.

Subject of Research: The causal and temporal effects of AI-powered platform government on screen-level bureaucratic responsiveness in Shenzhen, China.

Article Title: Lagged short-run efficacy, sustained long-run inefficacy: how the AI-powered platform government affects the screen-level bureaucratic responsiveness

Article References: Duan, C., Jia, Z., & Chen, S. (2026). Lagged short-run efficacy, sustained long-run inefficacy: how the AI-powered platform government affects the screen-level bureaucratic responsiveness. Global Public Policy and Governance, 6(2), 227-258. https://doi.org/10.1007/s43508-026-00149-9

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00149-9

Keywords: AI-powered platform government, screen-level bureaucrats, bureaucratic responsiveness, digital governance, e-government, online petitions, regression discontinuity in time, event-study method, natural language processing, organizational inertia, Shenzhen, public administration