Patient-Related Factors Associated With the Initiation of Potentially Inappropriate Medication in Home Care: An Observational Study Based on Resident Assessment Instrument Data

Background: The harmful outcomes of potentially inappropriate medications (PIMs) are highlighted among multimorbid older home care clients using several medicines. The aim of this study was to identify patient-related factors associated with the initiation of PIMs. Methods: This register-based study used Resident Assessment Instrument-Home Care (RAI-HC) assessments (n = 6176) from year 2014 to 2015. PIMs were identified according to the Beers criteria. Generalised estimating equations were used to identify factors associated with the initiation of PIMs. Findings: A total of 228 (11.3%) clients initiated PIMs during the follow-up (mean 13 months). Factors associated with higher odds to initiate PIMs were higher education (OR = 1.36, 95% CI 1.02–1.82), cognitive impairment (OR = 1.70, 1.02–2.82), reduced social interaction (OR = 1.50, 1.06–2.13), independent activity outdoors (OR = 1.72, 1.18–2.51), diabetes (OR = 1.47, 1.12–1.94), Parkinson's disease (OR = 3.42, 1.86–6.27) and longer interval between RAI assessments (OR = 1.09 per month, 1.02–1.18). Conclusions: Incidence of PIMs among home care clients was common. The results help healthcare professionals to focus more attention on clients more susceptible to PIM prescribing. Preventing PIM use is essential, especially among older adults with cognitive impairment, to prevent further decline of health status and admission to long-term care.Peer reviewe

Value creation and retention through re-servitization: Product service system for prescription medication dispensing in homecare

Product-service systems (PSS) typically assume that users determine the value of the service and take responsibility for deciding when to start and discontinue use. However, without visibility to organizational value such as fleet-level operational gains, PSS users are ill-equipped to make such decisions. In this study, we examine a PSS for dispensing prescription medication to memory-impaired homecare clients of a municipal healthcare organization. Robot dispensers provided by a PSS operator automate medication adherence duties, streamlining workflows and reducing nursing staff needed during morning peaks. However, neither the memory-impaired client nor the homecare provider have visibility to the service's value. Collaborating with the operator and the healthcare organization, we discovered that the main problem was the value that was lost when the dispenser was deployed and redeployed too late. As a solution design, we propose detection of lost value opportunity and dynamic capture of the available value using smart and connected product technology for the robotic dispenser and its simpler precursor alternative. With these changes, the PSS operator can continuously re-servitize, i.e. use the PSS to actively guide the deployment and redeploy the robotic dispensers for improved value impact. Our contribution to the innovation management is highlighting the role of re-servitization in unlocking the potential operational value of a PSS in dynamic environments, such as healthcare.Peer reviewe

Gluten-free diet during pregnancy and pregnancy outcome: A retrospective cohort study

Objective: A gluten-free diet (GFD) is becoming increasingly popular, especially among young females, and including those without diagnosed celiac disease (CD). Whether a GFD is appropriate during pregnancy remains unclear. Our primary aim was to evaluate the association of a GFD and neonatal birthweight and incidence of large for gestational age (LGA) and small for gestational age (SGA). Secondarily, we sought associations with other obstetric outcomes. Methods: The data was collected retrospectively from the Tampere University Hospital database. The study period was from January 2015 to April 2021. The diet information was obtained from self-reported questionnaires. All women following a GFD were included. A total of 79 had CD and 291 followed a GFD without CD diagnosis. The latter are referred to here as people without CD avoiding gluten (PWAG). A total of 456 omnivores were randomly chosen to constitute a control group. Outcomes were analyzed by comparing gluten-free groups to a control group. Results: The median birth weight was higher in the GFD group compared to the controls (3533 vs. 3440 g, P < 0.003), but the incidences of SGA or LGA did not differ between the study groups. The incidence of pregnancy complications was comparable between the groups. Induction of labor was more frequent (aOR 1.52; 95% CI: 1.12–2.08), and the duration of labor was longer (aOR1.56; 95% CI: 1.18–2.06) in the GFD group, especially among PWAG. However, no difference in the cesarean section rate were found between the groups. Conclusion: In the present retrospective cohort study, a GFD did not appear to be associated with adverse pregnancy or neonatal outcomes.Peer reviewe

Viranomaisten ja yhteisöjen rooli DfD-rakentamisessa

Rakennusalalla on kasvanut tarve kestävän kehityksen ja kiertotalouden mukaisten käytäntöjen omaksumiseen. Purettavaksi suunnittelu (Design for Disassembly, DfD) tarjoaa merkittäviä mahdollisuuksia vähentää rakennusjätteen määrää ja tukea materiaalien tehokasta uudelleenkäyttöä. DfD:n perusperiaate on suunnitella rakennukset siten, että ne voidaan purkaa elinkaarensa lopussa ja niiden osat voidaan käyttää uudelleen. Vaikka DfD:n hyödyt ovat selkeästi tunnistettu, sen laajamittainen käyttöönotto on hidasta sääntelyn puutteen ja taloudellisten kannustimien vähäisyyden takia. Tutkimuksessa tarkasteltiin tämänhetkistä lainsäädäntöä, standardointia ja haasteita koskien rakennusten purettavaksi suunnittelua. Tutkimuksen tavoitteena oli tutkia, millaisten toimenpiteiden ja kannusteiden avulla viranomaiset voivat vaikuttaa rakennusten purettavaksi suunnittelun käyttöönoton laajuuteen ja kannattavuuteen. Tutkimusmenetelmänä käytettiin kirjallisuustutkimusta ja aineisto kerättiin Web of Science -tietokannasta. Aineisto analysoitiin systemaattisesti. Tutkimuksessa tunnistettiin useita esteitä DfD-rakentamisen laajamittaiselle käyttöönotolle. Taloudelliset haasteet, kuten valikoivan purkamisen korkeammat kustannukset ja DfD:n vaatimat suuret alkuinvestoinnit, rajoittavat sen käyttöä, erityisesti tiukkojen budjettien ja aikarajojen vuoksi. Lisäksi sääntelyn ja standardien puute luo epävarmuutta alan toimijoille. Koulutuksen ja asiantuntemuksen puute oli myös yksi tunnistetuista esteistä. Lisäksi eri sidosryhmien heikko yhteistyö koetaan esteenä DfD:n kehitykselle. Tutkimustulosten mukaan DfD:n yleistyminen edellyttää tiukempaa lainsäädäntöä, selkeämpiä ohjeistuksia ja uusien standardien kehittämistä. Viranomaiset voivat tukea DfD-rakentamista luomalla käytännönläheisiä sääntöjä ja suosituksia, jotka selventävät sen soveltamista rakennusprojekteissa, sekä edistää uusien rakennusratkaisujen ja materiaalien käyttöönottoa. Lisäksi taloudelliset kannustimet, kuten verovähennykset ja investointituet purkamisen ja materiaalien uudelleenkäytön tukemiseksi, voivat tehdä DfD:n taloudellisesti houkuttelevammaksi. Viranomaisilla on myös tärkeä rooli yhteistyöverkostojen rakentamisessa muiden sidosryhmien kanssa, mikä edistää tiedonvaihtoa ja koulutusta DfD:n ymmärtämiseksi ja omaksumiseksi rakennusalalla. Tietoisuuden lisääminen ja koulutuksen tarjoaminen ovat myös keskeisiä toimenpiteitä, jotka voivat merkittävästi edistää DfD:n käyttöä ja hyväksyntää rakennusalan toimijoiden keskuudessa. Tutkimustuloksissa esiin tulleiden kehitysehdotusten perusteella työssä on laadittu ehdotus toimintasuunnitelmasta, jolla viranomaiset voivat edistää DfD-rakentamisen yleistymistä rakennusalalla. Toimintasuunnitelmassa ehdotetaan, että DfD:n käyttöönottoa lähestytään hallitusti ja vaiheittain, mikä mahdollistaa sen sujuvan integroimisen osaksi rakennusalan käytäntöjä. Ehdotetun toimintasuunnitelman avulla sääntely ja viranomaistoiminta kehittyvät rinnakkain rakennusalan uusien toimintatapojen kanssa. Tämä lähestymistapa tukee DfD:n tehokasta omaksumista ja sen laajempaa käyttöä tulevaisuudessa. Tutkimuksen tulokset tarjoavat suuntaviivoja sille, miten viranomaiset voivat edistää DfD:n käyttöönottoa. DfD voi toimia keskeisenä välineenä rakennusalan ympäristövaikutusten vähentämisessä ja kiertotalouden edistämisessä, mutta sen täysimittainen käyttöönotto vaatii viranomaisten aktiivista roolia ja lisää tutkimusta käytännön toimenpiteiden tueksi.In the construction industry, there has been an increasing need to adopt practices aligned with sustainable development and the circular economy. Design for Disassembly (DfD) offers significant opportunities to reduce construction waste and support the efficient reuse of materials. The fundamental principle of DfD is to design buildings so that they can be disassembled at the end of their life cycle and their components can be reused. Although the benefits of DfD are clearly recognized, its widespread adoption has been slow due to a lack of regulation and limited financial incentives. The research examined the current legislation, standardization, and challenges related to design for disassembly in buildings. The aim of the study was to investigate what kind of actions and incentives authorities can take to influence the extent and profitability of DfD adoption. The research method used was a literature review, and the data was collected from the Web of Science database. The data was systematically analyzed. The study identified several barriers to the widespread adoption of DfD construction. Economic challenges, such as the higher costs of selective demolition and the large initial investments required by DfD, limit its use, especially under tight budgets and deadlines. Additionally, the lack of regulation and standards, particularly regarding the use of recycled materials, creates uncertainty for industry stakeholders. A lack of training and expertise was also identified as one of the barriers. Furthermore, weak cooperation among different stakeholders is seen as an obstacle to DfD development. According to the research findings, the widespread adoption of DfD requires stricter legislation, clearer guidelines, and the development of new standards. Authorities can support DfD construction by creating practical rules and recommendations that clarify its application in construction projects and promote the adoption of new building solutions and materials. Moreover, financial incentives, such as tax reductions and investment subsidies for demolition and material reuse, can make DfD more financially attractive. Authorities also have an important role in building cooperation networks with other stakeholders, which fosters information exchange and training to increase understanding and acceptance of DfD in the construction industry. Raising awareness and offering education are also key measures that can significantly promote the use and acceptance of DfD among construction industry actors. Based on the development suggestions identified in the study, a proposal for an action plan has been developed, which outlines how authorities can promote the widespread adoption of DfD in the construction industry. The action plan suggests approaching DfD adoption in a controlled and phased manner, which allows its smooth integration into construction industry practices. With the proposed action plan, regulation and governmental activities will develop in parallel with the new practices in the construction industry. This approach supports the effective adoption of DfD and its broader use in the future. The results of the study provide guidelines for how authorities can promote the adoption of DfD. DfD can serve as a key tool in reducing the environmental impacts of the construction industry and promoting the circular economy, but its full-scale adoption requires active involvement from authorities and further research to support practical measures

Aurinkoenergiajärjestelmän tehon mallintaminen: Fysikaalisten ja koneoppimispohjaisten tehomallien vertailu

Joissain maissa aurinkoenergia on jo valtavirtaa, kun taas jossain maissa, kuten Suomessa, aurinkoenergia on vasta nousemassa aurinkovoimateknologian kehittyessä ja halventuessa. Jotta aurinkovoimalat toimisivat odotetusti, niiden tehon tuotantoa on tärkeää monitoroida vertaamalla todellista tehon tuotantoa mallinnettuun tehon tuotantoon. Koska yleisesti käytettyjen fysikaalisten tehomallien on näytetty antavan Suomen oloissa virheellisiä tuloksia esimerkiksi lumipeitteen ja alhaisen auringon säteilyn vuoksi erityisesti talvisin, työssä kokeillaan myös monimutkaisempia koneoppimispohjaisia tehomalleja. Aineistoa on viidestä aurinkoenergiajärjestelmästä neljästä eri sijainnista (Helsingistä, Turusta, Kuopiosta ja Sodankylästä). Tämän työn tavoitteena on tarkastella tehomallien virhettä hetkittäisessä tehon tuotannossa sekä energian tuotannon virhettä kuukausi- ja vuositasolla. Lisäksi työssä pyritään selvittämään, pystytäänkö koneoppimismalleilla mallintamaan tehoa tarkemmin kuin fysikaalisilla malleilla. Työssä tarkastellaan kolmea fysikaalista mallia ja kahta koneoppimismallia. Jotkin fysikaaliset mallit voidaan optimoida tiettyyn aurinkovoimajärjestelmään sopivaksi mikäli aiempaa aineistoa järjestelmästä on saatavilla. Tällaisia malleja kutsutaan tässä työssä täsmäkoulututetuiksi malleiksi. Jos aiempaa aineistoa ei ole saatavilla, on tehon mallintamiseen käytettävä yleisempiä fysikaalisia malleja, joita ei ole optimoitu kyseiseen järjestelmään. Tällaisia malleja kutsutaan tässä työssä yleisiksi malleiksi. Tulosten perusteella yleiset fysikaaliset mallit näyttäisivät mallintavan tehon systemaattisesti liian suureksi, mikä kasvattaa virheitä myös energian tuotannon mallintamisessa. Käytettyjen virhetermien mukaan yleiset koneoppimismallit mallintavat tehoa tarkemmin kuin yleiset fysikaaliset mallit. Yleisillä koneoppimismalleilla ei ole samanlaista taipumusta mallintaa tehoa aina liian suureksi, mutta koneoppimismallit näyttäisivät mallintavan alhaiset tehon arvot liian suuriksi ja suuret tehon arvot liian alhaisiksi. Koska osa virheistä kumoutuu, on kuukausittaiset ja vuosittaiset energian tuotannon mallintamisen virheet fysikaalisia malleja pienempiä. Täsmäkoulutus näyttäisi parantavan fysikaalisten ja koneoppimismallien tarkkuuksia ja vähentävän virheitä sekä tehon että energian tuotannon mallintamisessa. Täsmäkoulutuksen jälkeen toinen koneoppimismalleista antoi kaikista malleista pienimmät virhetermit, mutta tämän mallin kuukausittaiset ja vuosittaiset energian tuotannon mallinnuksen virheet yllättävästi kasvoivat pienentymisen sijaan. Vaikka koneoppimismallit näyttäisivät virhetermien ja energian tuotannon virheiden perusteella toimivan fysikaalisia malleja paremmin, ei mallien tarkkuuksien ero vaikuta olevan kovin suuri. Suurimmat erot fysikaalisten ja koneoppimismallien välillä näyttäisivät olevan mallien monimutkaisuudessa ja siinä, millainen systemaattinen taipumus yleisillä malleilla on tehon mallintamisessa. Koska koneoppimismallit tarvitsevat havaintoja useammasta muuttujasta toimiakseen, voi niiden käyttö olla hankalampaa etenkin pienemmillä aurinkoenergiajärjestelmillä, joissa on vähemmän mittalaitteita. Koneoppimismallien käyttö voi olla kuitenkin hyödyllistä esimerkiksi kaupallisilla aurinkovoimajärjestelmillä, joissa halutaan monitoroida tehoa mahdollisimman tarkasti tehomallin monimutkaisuudesta huolimatta.In some countries solar energy is already mainstream while in others, like Finland, it is gaining popularity due to advancements and decreasing costs in solar energy technologies. To ensure that the solar power plants operate as expected, it is essential to monitor them by comparing the expected power production to the actual power production. Because commonly used power models have been shown to produce erroneous results in Finland, more complex machine learning models are also implemented in this thesis. The data used is from five different power plants across four locations: Helsinki, Turku, Kuopio, and Sodankylä. The aim of this thesis is to study the errors that the models produce when modelling the instantaneous power productions, as well as the monthly and yearly energy yields. In addition, machine learning models are compared to physical models to determine if the added complexities of machine learning result in more accurate estimates. The comparison includes three physical models and two machine learning models. Some physical models can be optimized for a specific power system if data is available from that system. These models are referred to as fitted models. If previous data is not available, the models can’t be optimized to the system. These models are referred as general models. Results indicate that the general physical models systematically overestimate produced power, leading to increased errors in estimated energy yields. Based on the used error terms, general machine learning models predict power more accurately than general physical models. Machine learning models tend to overestimate low power values and underestimate high power values, causing some errors to cancel each other out and resulting in lower errors in estimated energy yields compared to physical models. Fitted models seem to be more accurate than general models, which is expected. One machine learning model outperformed all fitted models based on the error terms used. However, errors in monthly and yearly energy yield estimations increased unexpectedly. Although machine learning models appear to model power more accurately than physical models, whether fitted or general, the differences are small. The biggest differences between physical and machine learning models seem to be the complexities of the models and the systematic tendencies of general models. Because machine learning models require observations from more variables than physical models, their use can be more challenging, especially for smaller power plants with fewer measuring tools. However, the slight accuracy gains can be beneficial for larger commercial power plants despite the added model complexity

Katsaus verkkosivuluokitteluun neuroverkoilla

Alkeellista luokittelua on tehty jo vuosisatoja ja monikerroksisia neuroverkkoja on ollut useampi vuosikymmen. Internetin tullessa laajemmin kuluttajille sen kattavuuden ja si-sällön massiivisen kasvun yhteydessä huomattiin tarve verkkosivujen luokittelulle. Inter-net on ollut räjähdysmäisessä kasvussa ja siksi kehitystä on nähty paljon automaattisissa luokittelumetodeissa. Luokiteltavien asioiden lukumäärän ja niiden syvyyden kasvaessa on myös luokittelutarkkuuksien pysyttävä perässä, jolloin syntyy tarve myös luokittelu-metodien kehittymiselle. Neuroverkot ovat teknologiana jo tuttua aihepiirin tutkijoille, sillä ne tunnetaan entuudestaan niiden kuvien ja hahmojen luokittelukyvyistä. Tutkiel-man ideana on kartoittaa neuroverkkojen mahdollisia hyötyjä ja haittoja verkkosivu-luokittelussa. Tutkielma toteutetaan kirjallisuuskatsauksena ja sen aineistoa on kerätty laajasti aihepii-rille relevanteilta julkaisutietokannoilta. Valitut aineistot vaihtelevat iältään, mutta van-himmatkin lähteet ovat vain 1990-luvun loppupuolelta. Vanhempi aineisto toimii pohja-na ja tukee uudempaa relevanttia aineistoa. Neuroverkkoja hyödyntävästä luokittelusta ei ole aikaisemmin toteutettu vastaavanlaista listausta sen hyödystä ja haitoista verkkosi-vuluokittelullisessa kontekstissa. Neuroverkot loistavat verkkosivujen kuvaperäisessä luokittelussa, mutta ne vaativat kuitenkin suhteellisen paljon prosessointivoimaa verkkoa toteuttavalta laitteistolta

Clinical outcomes after revision knee arthroplasty due to periprosthetic joint infection: A single-centre study of 359 knees at a high-volume centre with a minimum of one year follow-up

PURPOSE: Decisions on the treatment of periprosthetic joint infection (PJI) are typically guided by established algorithms. However, as these algorithms often lack substantial supporting evidence, this study aimed to evaluate 1-year survival rates and compare different surgical approaches.METHODS: In this single-centre retrospective cohort study, all revisions of the knee due to PJI with at least 1 year of follow-up performed between January 2008 and September 2021 were identified. In total, 141 debridement, antibiotics, and implant retentions (DAIRs), 98 one-stage, and 120 two-stage revisions were performed. Infections were classified as early, acute hematogenous, or chronic infections. Survival was calculated using the Kaplan-Meier method and the cumulative incidence function. Predictors of outcomes were examined with Fine-Gray regression and Cox proportional hazards regression, and subdistribution hazard ratios (sdHR) and adjusted hazard ratios (aHR) with 95% confidence intervals (CIs) were calculated.RESULTS: At 1-year follow-up, 23% (CI 19%-27%) of patients had undergone a reoperation, and 4% (CI 2%-6%) had died. The risk of reoperation was largest after two-stage revision (28%, CI 20%-36%) and smallest after one-stage revision (15%, CI 9%-23%). For every infection type, the failure rates at one-year follow-up favoured one-stage revision over two-stage revision. Higher ASA-scores increased the risk of death (aHR 1.7, CI 1.1-2.5 per one-unit increase).CONCLUSION: The risk of failure after one-year follow-up is high after revision for periprosthetic joint infection. The lowest risk was observed after one-stage revision; however, this may partly reflect patient selection, as one-stage revision may not be suitable for all patients.LEVEL OF EVIDENCE: Level III, retrospective comparative study.Peer reviewe

Using Physics-Informed Neural Networks for Modeling Biological and Epidemiological Dynamical Systems

Physics-Informed Neural Networks (PINNs) have emerged as a powerful approach for integrating physical laws into a deep learning framework, offering enhanced capabilities for solving complex problems. Despite their potential, the practical implementation of PINNs presents significant challenges. This paper explores the application of PINNs to systems of ordinary differential equations (ODEs), focusing on two key challenges: the forward problem of solution approximation and the inverse problem of parameter estimation. We present three detailed case studies involving dynamical systems for tumor growth, gene expression, and the SIR (Susceptible, Infected, Recovered) model for disease spread. This paper outlines the core principles of PINNs and their integration with physical laws during neural network training. It details the steps involved in implementing PINNs, emphasizing the critical role of network architecture and hyperparameter tuning in achieving optimal performance. Additionally, we provide a Python package, ODE-PINN, to reproduce results for ODE-based systems. Our findings demonstrate that PINNs can yield accurate and consistent solutions, but their performance is highly sensitive to network architecture and hyperparameters tuning. These results underscore the need for customized configurations and robust optimization strategies. Overall, this study confirms the significant potential of PINNs to advance the understanding of dynamical systems in biology and epidemiology.Peer reviewe

Streaming Optimization in Web Engine-based Video Player for Television Application

This thesis investigates optimization techniques for web engine-based video players specifically designed for Smart TV platforms. As streaming becomes the dominant form of television content delivery, the technical challenges of delivering high-quality video experiences on resource-constrained Smart TV hardware have become increasingly significant. This research focuses on the optimization of streaming technologies, particularly HTTP Live Streaming (HLS) and Dynamic Adaptive Streaming over HTTP (DASH), within the context of WebOS and Tizen operating systems that dominate the Smart TV market. This study employs a structured methodology to develop and evaluate a web engine-based video player, addressing key performance challenges such as buffering time reduction, track switching optimization, and adaptive streaming efficiency. The research methodology integrates quantitative performance metrics and qualitative user experience assessments to evaluate the effectiveness of various optimization techniques. The study also emphasizes the integration of Digital Rights Management (DRM) technologies and their impact on playback performance. The implemented video player demonstrates significant improvements over baseline implementations, with a 42% reduction in initial buffering time, 37% decrease in rebuffering frequency, and 28% more stable quality transitions during network fluctuations. These improvements are achieved through a combination of platform-specific optimizations, buffer management strategies, and adaptive bitrate algorithms tailored for television applications. Results indicate that buffer-based adaptation approaches generally outperform throughput-based algorithms on Smart TV platforms, particularly when optimized for specific rendering engine characteristics. This research contributes to the field by providing empirical evidence for the effectiveness of specific optimization techniques in television applications and developing a framework for evaluating streaming performance across different Smart TV platforms. The findings have practical implications for application developers, content providers, and platform manufacturers working to improve streaming experiences on Smart TV devices. Future research directions include exploring machine learning-based adaptation strategies and hybrid native/web approaches for further performance enhancements

Integration of environmental management systems into the planning phase of golf course development in Finland

This thesis investigates the integration of environmental management systems (EMS) into the planning phase of golf course development in Finland. Golf courses have the opportunity to serve as valuable green spaces and as important biodiversity habitats, but their development often creates environmental challenges related to resource consumption, chemical use and landscape alternation. Despite the growing environmental awareness in the golf industry, systematic approaches to environmental management are typically implemented during operational phases rather than being integrated from the earliest planning stages. This thesis examines current EMS implementation practices in Finnish golf development and identifies opportunities and challenges for integrating environmental management considerations during the planning phase of golf course development. To understand current practices and the opportunities and challenges of EMS implementation, data was collected through content analysis of EMS frameworks promoted in the Finnish golf industry and semi-structured interviews with five golf industry’s internal stakeholders, including golf course CEO, professional greenkeepers and course designers. Interview data was analysed thematically to identify stakeholder perspectives on EMS integration opportunities and challenges. The examined theoretical frameworks included Golf Course 2030 by the R&A and GEO Sustainable Golf Development Voluntary Sustainability Standard for Development by GEO Foundation for Sustainable Golf. The results revealed implementation gaps between theoretical EMS frameworks and practical application in golf course development. While all stakeholders demonstrated high environmental awareness, EMS knowledge remained limited to one golf-specific operational program, limiting the opportunity to explore alternative frameworks that might be more compatible. The study identified structural barriers in EMS implementation, particularly the late involvement of golf industry stakeholders in site selection decisions, which creates permanent constraints on environmental performance in the course. Additional challenges included transportation-related emissions and remote locations, limited organizational resources and cultural expectations. Stakeholders reported positive economic outcomes from existing EMS implementation, including improved financial terms and operational cost reductions. While the seasonal nature of Finnish golf was initially perceived as a constraint, stakeholders found opportunities that can support diverse land use and community access during off-season. The findings of this thesis suggest that successful EMS integration into golf course planning requires tackling structural constraints in development processes to ensure early involvement of golf industry expertise and supporting cultural shifts toward more sustainable practices