The technological heart of the study is Multi-Temporal Interferometric Synthetic Aperture Radar, or MT-InSAR, a technique that has transformed how scientists monitor ground deformation. Synthetic aperture radar satellites bounce microwave signals off the Earth’s surface and record the phase of the returning waves. When the same point on the ground is imaged repeatedly over months or years, tiny changes in the distance between the satellite and the surface shift the phase of the reflected signal. By comparing these phase differences across many acquisitions, researchers can measure ground movement with millimetre-level precision, without ever setting foot on the unstable slope. The Cyprus team processed both ascending and descending orbit datasets, meaning the satellite flew over the study area from different directions, which allowed them to sample the movement of the ground along two distinct lines of sight and to build a more complete picture of the three-dimensional deformation field.

The researchers applied this technique to landslides already catalogued in an existing inventory of southwestern Cyprus, covering slopes classified as active, dormant and relict. This classification matters because it reflects the state of a landslide at the time of mapping: active landslides are currently moving, dormant ones have not moved recently but retain the potential to reactivate, and relict ones are ancient features formed under different climatic conditions that are generally considered stable. The team extracted the Line-of-Sight, or LOS, velocities for each landslide from the radar data and then asked whether the measured rates differed systematically between these activity classes. The answer was emphatic. The largest LOS velocities were observed in landslides characterised as active, followed by dormant and then relict landslides, a hierarchy that confirms the radar measurements are capturing genuine differences in slope behaviour rather than noise.

That validation is more important than it might sound. One of the persistent challenges in landslide science is that inventories are often compiled from aerial photographs, historical records and field visits, all of which involve a degree of subjective judgement. If satellite-derived velocities align cleanly with independently assigned activity classes, it suggests both datasets are reliable and, more intriguingly, that radar could in future be used to update and refine inventories automatically. For a country like Cyprus, where development continues to push onto marginal slopes, an objective, repeatable measure of which landslides are truly moving could directly inform planning decisions and early-warning strategies. The study effectively demonstrates that the velocity signal from space carries real diagnostic information about the state of a landslide.

The second major strand of the analysis asked which materials move fastest, and here the geology of Cyprus provides a striking answer. The largest displacement rates corresponded mostly to outcrops of fine-grained clastic rocks, including marls, radiolarian mudstones and mélange. These rock types share a critical property: weak shear strength. Marls, which are mixtures of clay and calcium carbonate, are notorious in engineering geology because they swell when wetted, slake when dried and lose strength dramatically as pore water pressure rises. Radiolarian mudstones and the chaotic, mixed rock assemblages known as mélange behave similarly, deforming readily under stress. When such weak materials sit on a slope, the resistance to sliding is low, and once movement begins, the ground can creep steadily for years. The radar data confirmed that these lithologies host the fastest-moving terrain in the study area, tying the observed deformation directly to the mechanical properties of the bedrock.

To move beyond simple correlations, the team turned to a machine-learning interpretability tool called Shapley Additive Explanations, or SHAP. Borrowed from cooperative game theory, SHAP analysis quantifies how much each input variable contributes to a model’s prediction for any individual case, distributing credit among factors in a way that is mathematically consistent. In the context of this study, the method allowed the researchers to rank the influence of geological, geomorphological, environmental and geotechnical factors on the measured LOS velocities. The headline finding was unambiguous: rainfall emerged as the primary factor influencing the increase in LOS velocity. In other words, while many ingredients conspire to create a landslide, precipitation is the dominant throttle controlling how quickly the ground moves once instability is established.

This result carries a profound implication for how landslide hazard should be understood and managed. The geological and geotechnical characteristics of a site determine whether a landslide can occur at all; they set the stage by providing weak materials, favourable orientations and susceptible slope geometries. But once a landslide has been initiated, the tempo of its movement is governed largely by the water delivered from above. Rainfall infiltrates the slope, raises pore water pressures within the sliding mass, reduces the effective stress that holds the material together and lubricates the failure surface. The Cyprus findings suggest that monitoring rainfall and incorporating precipitation records into deformation forecasts could substantially improve predictions of when slow-moving slides will accelerate, which is precisely the information that engineers and emergency managers need most.

Among the geotechnical indices examined, two stood out. The Plasticity Index, or PI, a standard measure of how much water a soil can absorb before it transitions between consistency states, generally exhibited a positive correlation with ground velocity. High-PI clays are more compressible, more prone to swelling and shrinkage, and more sensitive to moisture changes, so it makes intuitive sense that slopes founded on such materials move faster. Conversely, the Geological Strength Index, or GSI, a qualitative scale that characterises the overall strength of a rock mass based on its structure and the condition of its discontinuities, demonstrated a weak negative correlation with velocity. Stronger, more intact rock masses move more slowly, as expected, though the weakness of the correlation suggests that intact rock strength plays a secondary role compared with moisture conditions and the intrinsic weakness of fine-grained units.

The choice of Cyprus as a case study gives the work practical weight well beyond the island. Southwestern Cyprus is underlain by the Mamonia Complex and related units, a tectonically jumbled assemblage of weak sedimentary rocks that has produced some of the island’s most problematic ground. Landslides in this region have damaged roads and infrastructure for decades, and the Geological Survey Department of Cyprus, whose scientists provided the geological and geotechnical data underpinning the study, maintains detailed records of the affected areas. The research was funded through the European Union’s Horizon 2020 programme under a Marie Skłodowska-Curie grant, reflecting the European dimension of the problem: weak, clay-bearing formations and intensifying rainfall extremes threaten slopes across the entire Mediterranean basin, from Italy and Greece to Turkey and North Africa.

What makes the study resonate beyond its regional focus is the methodological template it offers. By fusing freely available satellite radar data with conventional geological mapping and laboratory-derived geotechnical indices, and by interrogating the relationships with a modern interpretability framework, the researchers have shown how the pieces of the landslide puzzle can be assembled quantitatively rather than anecdotally. The approach requires no expensive ground instrumentation, can be repeated as new satellite acquisitions arrive, and scales naturally from a single slope to an entire country. As climate change intensifies rainfall variability across the Mediterranean and elsewhere, the ability to identify which slopes are moving, how fast, and what drives their acceleration will become an essential tool for protecting communities. The Cyprus study suggests that the answer, at least for the fastest-moving hazards, may literally be falling from the sky.

Subject of Research: Statistical correlation of InSAR-derived landslide ground movement rates with activity class and geological, geotechnical and climatic causal factors in southwestern Cyprus

Article Title: Correlating InSAR-derived ground movement rates with landslide activity class and causal factors: a case study from Cyprus

Article References: Tzampoglou, P., Loukidis, D., Tolomei, C., & Svigkas, N. (2026). Correlating InSAR-derived ground movement rates with landslide activity class and causal factors: a case study from Cyprus. Scientific Reports. https://doi.org/10.1038/s41598-026-74387-6

Image Credits: AI Generated

DOI: 10.1038/s41598-026-74387-6

Keywords: InSAR, landslides, Cyprus, MT-InSAR, rainfall, SHAP analysis, geotechnical indices, Plasticity Index, Geological Strength Index, marls, remote sensing, natural hazards

The problem with the Cypriot waste bentonite is written into its chemistry. Bentonite owes its worldwide usefulness to smectite clay minerals, which swell dramatically when hydrated and form stable colloidal gels. Premium sodium bentonites, with more than 80 percent sodium-rich smectite, can boast specific surface areas of 600 to 800 square meters per gram, mostly hidden inside the interlayer space between clay platelets. The Polycanthos material, however, is dominated by potassium: potassium oxide reaches up to 3 percent by weight, and potassium ions lodged in the interlayer spaces bind the clay layers together far more tightly than sodium or calcium would, inhibiting the swelling on which everything else depends. The clay also carries up to 17 percent ferric iron in its octahedral layers, which is known to impair surface hydration, and its smectite content of 59 to 68 percent is diluted by 15 to 24 percent of inert, abrasive quartz and amorphous opal-CT silica.

Worse still, the mineralogy is complicated by interstratified illite/smectite layers, a mixed-layer structure in which non-expanding illite sheets are interleaved with smectite. These illite layers physically block full hydration and platelet separation, which is why conventional sodium carbonate activation, the standard industrial trick for swapping unfavorable cations for sodium, fails to deliver the rheological and filtration behavior demanded by American Petroleum Institute specifications. In a drilling well, that failure is not academic. Fluids that lose their colloidal structure at depth cannot carry rock cuttings to the surface, and at temperatures approaching 180 degrees Celsius in medium-to-high-depth wells, bentonite dispersions routinely fall out of compliance and flocculate, threatening stuck pipe and abandoned operations.

The research team, building on their earlier demonstration that the waste bentonite could be activated with sodium carbonate and strengthened with xanthan gum, cast a wide net. They screened eight polymers spanning the three major ionic classes: anionic additives including xanthan gum, carboxymethyl cellulose (CMC), polyanionic cellulose regular (PAC-R), and polyacrylamide; nonionic modifiers including guar gum, polyethylene glycol dimethyl ether, and maize starch; and cationic candidates in the form of polyacrylamide and dodecylpyridinium chloride. In total, 39 drilling fluid systems were prepared in 350-milliliter laboratory barrels, each containing 22.5 pounds of the activated waste clay, barite as a weighting agent, sodium carbonate as activator, and sodium hydroxide for pH control, with polymer doses varied systematically. A benchmark fluid made from commercial sodium bentonite from Milos, Greece, provided the yardstick.

The cationic polymers failed immediately and instructively. Their positively charged groups displace potassium ions through cation exchange, but the hydrophobic nature of their polymer backbones promotes aggregation rather than dispersion in water, producing instant phase separation instead of a stable colloid. Maize starch and polyethylene glycol were only partially effective, demanding roughly 150 grams and 70 grams per batch respectively, quantities so large they were ruled impractical. That left the anionic family and guar gum as the serious contenders, and the team subjected them to a battery of tests: rheology on a Fann Model 35A Couette viscometer fitted to both Bingham Plastic and Herschel-Bulkley models, low-pressure low-temperature filtration at 100 psi, and structural analysis by X-ray diffraction, scanning electron microscopy with energy-dispersive spectroscopy, and infrared spectroscopy.

The anionic polymers worked through a mechanism the authors describe as electrosteric stabilization. Negatively charged polymer chains adsorb onto the edges of clay platelets while their ionized carboxylate groups extend into the water, generating electrostatic and steric repulsion that prevents aggregation and builds the colloidal network. But the efficiency of this process turned out to be governed by the illite/smectite transformation itself. X-ray diffraction revealed peak splitting characteristic of interstratified clays, and energy-dispersive analysis of the silicon-to-aluminum ratio showed that PAC-R induced the strongest beidellite formation, with the broadest ratio at 6.214 and the lowest calcium and magnesium concentrations. Beidellite, with its highly negative tetrahedral charge, creates expanded clay frameworks that demand more polymer to bridge, which is why PAC-R required the largest dose to meet the API shear-stress threshold of 15.31 pascals at maximum shear rate.

CMC emerged as the quiet champion. Its linear conformation, high degree of dissociation, and the comparatively weak illite/smectite transformation in its systems allowed it to satisfy API rheology and filtration standards with the lowest polymer input of any additive tested. Its 10-second gel strengths ranged from 0.5 to 4 pascals and its 10-minute gel strengths from 0.75 to 11.5 pascals, both comfortably inside the operationally safe window of roughly 4.8 to 14.3 pascals, strong enough to suspend cuttings when circulation stops but weak enough to restart flow without fracturing the formation. Its filter cake was exceptionally tight, with a permeability of about 0.39 millidarcy and a filtrate loss of just 7.2 milliliters, well under the 15-milliliter API limit. PAC-R produced the densest cake of all at 0.14 millidarcy, while xanthan gum’s branched structure left a more porous cake and slightly higher fluid losses at low doses.

The decisive test came with heat. The optimized fluids were cycled from 40 to 90 degrees Celsius using a roller oven and thermostatically controlled viscometer cup, simulating the thermal conditions of upper well sections. Plastic viscosity declined with temperature in every system, as thermal energy accelerates deprotonation of clay surfaces and polymer chains, disrupting gel structures. PAC-R collapsed entirely, losing colloidal function at elevated temperatures. Guar gum, whose weak glycosidic bonds are prone to thermal scission and oxidative degradation, faded as temperatures climbed, though a modest dose increase could make it viable. Xanthan gum retained API compliance across most of the range, but CMC kept the strongest colloidal network of all, holding its rheological parameters above the compliance threshold up to 90 degrees Celsius and maintaining robust yield points throughout. When compared against both the Milos commercial bentonite and published data on Wyoming sodium bentonite, the CMC- and xanthan-amended composites performed adequately, validating the transformation of a problematic waste into a genuine drilling fluid component.

The economics seal the argument. A laboratory-scale formulation of the CMC system costs an estimated 0.81 euros per unit, with the xanthan version at 0.88 euros, and the clay itself is essentially free, sitting in stockpiles at the quarry while the quarry’s water-recycling system reuses the distilled water. Those figures are preliminary, technology-readiness-level-four estimates that bulk procurement should only improve, but the strategic logic is compelling: converting a low-value waste stream into a high-performance material reduces reliance on imported bentonite at a time of rising European prices, minimizes landfilling, and aligns squarely with the circular-economy principles of the European Commission’s Green Deal. The authors suggest next steps including dynamic thermal-aging tests, formulation with field waters such as seawater, and Raman spectroscopy to probe clay-polymer interactions at the molecular level. For now, the message is strikingly simple: the answer to one of drilling engineering’s nagging supply problems may have been sitting in a Cypriot waste heap all along, waiting for the right polymer to unlock it.

Subject of Research: Polymer modification of potassium-rich waste bentonite for thermally stable water-based drilling fluids

Article Title: Environmentally friendly polymer-modified K + -rich waste bentonite composites for achieving colloidal and thermal stability in water-based drilling fluids

Article References: Ramsis, Y., Sarris, E.-N., Papadopoulou, L., & Kantiranis, N. (2026). Environmentally friendly polymer-modified K+-rich waste bentonite composites for achieving colloidal and thermal stability in water-based drilling fluids. Case Studies in Chemical and Environmental Engineering, 14, Article 101497. https://doi.org/10.1016/j.cscee.2026.101497

Image Credits: AI Generated

DOI: 10.1016/j.cscee.2026.101497

Keywords: bentonite, drilling fluids, polymers, carboxymethyl cellulose, xanthan gum, rheology, thermal stability, circular economy, Cyprus, clay minerals, Eastern Mediterranean gas, API standards

The researchers focused on Cyprus, a country that maintained detailed surveillance data across the first four waves of the COVID-19 pandemic. Each confirmed case in their dataset carried a set of epidemiological attributes, including age, gender, date of infection, residency, and a classification of the case according to NACE, the European statistical classification of economic activities that encodes occupational context. Cases that had been linked by contact tracing formed the observed edges of an infection network, while the unlinked cases represented the missing structure the team hoped to recover. Framing the problem this way turns source attribution into a link prediction task: given two cases, can a model estimate how likely it is that one infected the other, even when no tracer ever documented that connection?

The first of the two approaches evaluated in the study relied on classical machine learning classifiers trained on engineered edge-level features. For every candidate pair of cases, the researchers combined the node-level attributes of the two individuals using a variety of operators, producing a feature vector that described the pair rather than either case alone. This design allowed the models to learn patterns such as whether infections were more likely between people of similar ages, overlapping infection dates, the same region of residence, or compatible occupational categories. Because the raw feature space constructed from all these combinations was large, the team applied Minimum Redundancy Maximum Relevance, or mRMR, to select features that were strongly associated with true transmission links while avoiding redundant information, and then refined the selection with greedy forward feature selection. Permutation importance was subsequently used to quantify how much each retained feature contributed to the classifiers’ decisions.

Among the classifiers tested, Random Forest and Gradient Boosting consistently delivered the strongest performance. During the first two pandemic waves, these models achieved F1-scores ranging from approximately 0.82 to 0.90, indicating a strong balance between precision and recall in distinguishing plausible transmission pairs from implausible ones. Mean Reciprocal Rank values, which measure how highly the true infector appears in a model’s ranked list of candidate sources, ranged from 0.55 to 0.64 for those early waves. In practical terms, this means that for a substantial share of unlinked cases, the correct or nearly correct infector sat at or near the top of the model’s shortlist, exactly the kind of ranking an epidemiologist would need to prioritize follow-up investigations.

Performance declined in the later waves, and the decline is itself informative. For the third and fourth waves, F1-scores ranged from 0.68 to 0.75 and MRR values dropped to between 0.23 and 0.33. The authors’ results suggest that the structure of transmission changed as the pandemic evolved: later waves in Cyprus were shaped by widespread community transmission, variants of concern, and vaccination, all of which blur the simple epidemiological signatures, such as tight date proximity or shared workplace, that made early-wave links easier to predict. When many candidate infectors share similar attributes with a case, the ranking task becomes fundamentally harder, and the numbers reflect that added ambiguity rather than any weakness in the method itself.

The second approach moved beyond hand-engineered features and turned to graph representation learning. The team employed a GraphSAGE model, a neural architecture that generates node embeddings by aggregating information from a node’s attributes and from the topology of the observed network around it. Each case is thus represented as a dense vector that encodes both what the case looks like epidemiologically and where it sits in the web of known connections. To score a candidate link between two cases, the researchers evaluated several strategies for combining the two embeddings, including concatenation, absolute difference, squared difference, the Hadamard product, and the dot product, each of which captures a different notion of similarity or compatibility between the pair.

Link prediction with graph representation learning achieved F1-scores ranging from roughly 0.70 to 0.79 and MRR values from 0.23 to 0.42 across all four pandemic waves. Compared with the classical classifiers, the GraphSAGE approach was somewhat less accurate in the early waves, where the engineered features captured the epidemiological signal very effectively, but it offered a more uniform level of performance across the entire pandemic period. This consistency matters for public health practice, because a tool that degrades sharply between epidemic phases is harder to integrate into routine surveillance workflows than one whose reliability is more stable. The embedding-based method also has the advantage of learning representations directly from the network, potentially capturing relational patterns that a fixed feature-engineering pipeline would miss.

The most consequential step of the study came after validation. Having confirmed that the best-performing classifier could reliably recover known links held out from the training data, the researchers applied it to the genuinely unlinked cases in the Cypriot dataset, asking the model to infer the most likely infector for each isolated node. The resulting reconstructed networks showed markedly fewer isolated components and larger connected structures than the original traced networks. Importantly, the reconstruction preserved epidemiologically relevant characteristics of the network, most notably the outdegree distribution, which reflects how many secondary infections are attributed to each case. Maintaining this property indicates that the inferred links did not distort the underlying transmission dynamics, such as the presence of superspreading individuals, but instead extended the observed network in a plausible direction.

The implications of this work extend well beyond one country or one pathogen. Incomplete infection networks are a chronic feature of outbreak response, from foodborne disease investigations to emerging zoonotic threats, and the quality of public health decisions depends heavily on how accurately those networks reflect reality. A reconstructed network with fewer isolated cases gives epidemiologists a more complete picture of transmission chains, enabling more targeted interventions: contact investigations can be prioritized toward the candidate sources ranked highest by the model, and containment resources can be directed at the connection points that matter most for interrupting spread. The authors note that such tools are particularly valuable during small epidemic waves, when case numbers are limited and every unexplained infection carries a proportionally larger informational cost.

The study also illustrates a broader lesson in applied machine learning for public health: classical feature-based models and modern graph neural networks are complementary rather than competing tools. Engineered epidemiological features, when chosen carefully with methods like mRMR and validated with permutation importance, can deliver excellent performance when the transmission signal is strong and interpretable. Graph representation learning, by contrast, offers robustness across changing epidemic conditions and the flexibility to incorporate network structure directly. As surveillance systems grow richer and more digitized, hybrid pipelines that combine both strategies, validated rigorously on known links before being trusted with unknown ones, are likely to become a standard component of the computational epidemiology toolkit, turning the frustrating gaps in contact tracing data from dead ends into testable hypotheses about the hidden architecture of disease spread.

Subject of Research: Link prediction in COVID-19 infection networks using machine learning and graph representation learning

Article Title: Predicting missing links in COVID-19 infection networks

Article References: Dimitriou, P. A., Silvestros, V., Constantinou, E., Pitris, C., & Kolios, P. (2026). Predicting missing links in COVID-19 infection networks. PLOS Complex Systems, 3(8), e0000124. https://doi.org/10.1371/journal.pcsy.0000124

Image Credits: AI Generated

DOI: 10.1371/journal.pcsy.0000124

Keywords: COVID-19, link prediction, machine learning, graph representation learning, GraphSAGE, contact tracing, epidemiology, Cyprus, Random Forest, Gradient Boosting, network reconstruction, public health

Primary health care, anchored internationally in the Declaration of Astana, emphasizes person-centred services across the lifespan, spanning preventive, promotive, curative, rehabilitative and palliative care, alongside intersectoral action on the social determinants of health. Occupational therapy, with its holistic, client-centred and occupation-based approach, would seem a natural fit for this agenda, and the profession worldwide is gradually shifting from hospital rehabilitation toward health promotion and population health. Yet integration remains limited almost everywhere, held back by structural and historical barriers, weak funding mechanisms and the profession’s traditional orientation toward curative rehabilitation. In Cyprus these problems are amplified by an ongoing health system reform and a very young educational infrastructure, since the country’s only occupational therapy programme admitted its first cohort as recently as 2015.

The Cypriot context gives the study its urgency. Since 2019, the General Health System, known as GeSY, has reorganized care around personal doctors acting as gatekeepers, with the Health Insurance Organization purchasing services and an explicit emphasis on prevention and integrated care. Rehabilitation services, including occupational therapy, are nominally part of the GeSY benefits package. In practice, however, most occupational therapists work in hospitals, rehabilitation centres or private practice, and the most recent survey by the Council of Occupational Therapists for the European Countries counted only 43 occupational therapists employed in primary health care in Cyprus. By contrast, countries such as Norway, the Netherlands and the United Kingdom report between 700 and 1,400 occupational therapists working in primary care, supported by clear referral pathways and insurance-based funding. Meanwhile, Cyprus faces rising chronic disease, obesity, smoking and physical inactivity, including among children, trends that demand exactly the community-based preventive workforce the country lacks.

To explore how students experience this disconnect, the researchers ran a two-phase qualitative study with seven undergraduate students, four women and three men, six of them in their fourth year and one in her third, all of whom had completed at least 340 hours of clinical placement. In the first phase, a 70-minute focus group was conducted using a semi-structured guide, with participants given translated stimulus materials including the curriculum itself, the Declaration of Astana, the Cyprus Country Health Profile and the European Tuning Competency Framework for Occupational Therapy. The transcript was analysed using Framework Analysis, a method that combines predefined themes from the literature with participant-driven insights through systematic coding, charting and interpretation. In the second phase, the same students took part in a Nominal Group Technique session, a structured consensus method in which participants silently generate ideas, share them in round-robin fashion, discuss and clarify them, and then rank their top five choices using a weighted point system.

The focus group findings coalesced around five themes, and their content is striking. Students described a curriculum rich in theory but heavily skewed toward paediatric and adult neurological rehabilitation, with minimal exposure to older adults, palliative care, prevention or underserved populations. One participant noted that courses discuss stroke extensively but never what someone can do to avoid having a stroke in the first place. Students felt confident managing conditions after onset but unprepared for proactive and end-of-life interventions, a serious gap in a country with a rapidly ageing population and high rates of chronic disease. They also reported that their skills were concentrated in one-to-one and small-group clinical work, leaving them unsure of their role in public health and population-level empowerment, even as they expressed a clear desire for project-based, community-engaged learning in which they could co-design initiatives with underserved groups.

Two further themes deepened the picture. On multisectoral action, students said they understood interdisciplinary collaboration in theory but felt practically unprepared to function within cross-sectoral systems, attributing this to the absence of shared educational experiences with other health professions. One participant observed that while occupational therapy students understand their own role, other team members cannot understand theirs, partly because the disciplines never train together, and another noted that other degrees in the faculty lack interprofessional education entirely, making collaboration structurally difficult. On autonomy, students reported a persistent gap between knowing and doing: theoretical concepts from occupational science were intellectually valuable but hard to internalize without application, and even laboratory exams remained, in their words, entirely theoretical. This lack of integration undermined their clinical reasoning, professional autonomy and confidence in unfamiliar settings.

The Nominal Group Technique session translated these frustrations into a ranked set of fifteen concrete recommendations, which clustered into three pedagogical categories. Feedback-based teaching received the highest priority, with students demanding specific, meaningful feedback delivered at multiple stages of each course, in both written and verbal form, rather than generic comments. Applied learning came next: case studies integrated across all courses, simulation-based learning incorporating tools such as artificial intelligence and trained actors for realism, and project-based learning involving design-thinking activities in real-world contexts. One participant pointedly invoked medical education’s simulation dolls, where students examine, diagnose and decide on the spot, and asked why occupational therapy students should not train the same way. Collaborative, interprofessional teaching ranked third, reflecting a strong desire to learn alongside nurses, general practitioners and other peers in structured ways that build genuine teamwork capacity. Peer mentoring, in which senior students guide juniors, was also valued as a scaffold for professional development.

The authors situate these findings within a broader international debate. Students in other countries have similarly reported feeling underprepared for community-based roles because curricula remain grounded in biomedical rehabilitation models, and global frameworks, including the World Federation of Occupational Therapists’ workforce strategy and the WHO Rehabilitation Competency Framework, all call for competencies in community engagement, interprofessional collaboration, leadership and advocacy. Examples from the Global South show what transformation can look like: Brazil’s reforms emphasize community-based practice aligned with national health goals, while South Africa’s occupation-based community development framework positions therapists as partners who co-design interventions with communities to address occupational injustice. The study also highlights a pragmatic tension that risks reinforcing the status quo: even when broader learning opportunities exist, students feel compelled to pursue conventional placements because jobs in prevention and health promotion simply do not exist, meaning idealistic curriculum choices carry no labour-market reward.

The study’s limitations are acknowledged candidly. With only seven participants from a single institution, transferability is constrained, and the exclusive reliance on student perspectives, without triangulation with educators or curriculum documents, limits confirmability, while the absence of longitudinal follow-up leaves open how perceptions of readiness might evolve. Nevertheless, the researchers argue the message is clear and consequential: occupational therapy education in Cyprus is theoretically strong but misaligned with evolving societal health priorities, and reform is needed to embed primary health care competencies, experiential and interprofessional learning, and community engagement throughout the curriculum. As one participant put it, if the university does not change how it prepares students to work in prevention and health promotion in areas where the profession is still underdeveloped, then change will never happen. For a profession whose founding values align so closely with primary health care, the students’ verdict amounts to a challenge not just to one small programme, but to occupational therapy education everywhere.

Subject of Research: Occupational therapy students' perceptions of their readiness for primary health care and curriculum reform

Article Title: Student’s views on their education – Are they ready for primary health care?

Article References: Orologas, S., Sakellariou, D., Christensen, J. R., & Björklund Carlstedt, A. (2025). Student’s views on their education – Are they ready for primary health care?. Scandinavian Journal of Occupational Therapy, 32(1), Article 2597208. https://doi.org/10.1080/11038128.2025.2597208

Image Credits: AI Generated

DOI: 10.1080/11038128.2025.2597208

Keywords: occupational therapy, primary health care, curriculum development, health professional education, Cyprus, interprofessional education, simulation-based learning, community-based practice, health promotion, professional identity, qualitative research, Nominal Group Technique