High Level Of Utilization Research Articles

Variations in health literacy and influential factors affecting the categories of social support among rural patients with diabetes mellitus

ObjectiveThe aim of this study is to explore the inherent classification of social support among individuals residing in rural areas of China. Additionally, we aim to examine the attributes and variations in health literacy scores among patients with diabetes mellitus (DM) within diverse social support categories.DesignCross-sectional study.MethodsEmploying the multi-stage stratified sampling technique, we enrolled 2,178 patients diagnosed with DM residing in the rural regions of Guangxi Province. We utilized the General Information Questionnaire, Social Support Rating Scale (SSRS), and Functional, Communicative and Critical Health Literacy Instrument.ResultsThe rural patients with DM were categorized into four distinct groups based on the types and levels of their underlying social support. These groups included a low-level social support utilization group (43%), a low-level objective social support group (17%), a moderate-level social support group (20%), and a high-level social support and high-level utilization group (20%). Statistical analysis revealed significant differences among the four groups in terms of age, disease duration, and blood sugar control level (p < 0.05). Furthermore, health literacy scores and scores across various dimensions for rural patients with DM demonstrated variability in accordance with latent profiles of social support, with statistically significant differences observed (p < 0.05). A positive correlation was identified between the level of social support and all dimensions of health literacy among rural patients with DM (p < 0.05).ConclusionThe social support available to individuals with DM in rural settings can be categorized into four distinct types, and its manifestation is influenced by demographic factors. The health literacy of rural patients with DM is intricately linked to the extent of social support they receive. For enhanced outcomes, interventions targeted at enhancing health literacy and quality of life among rural patients with DM should be tailored to address the heterogeneity observed in latent profiles of social support.

Machine-learning based prediction for high health care utilizers using a multi-institution registry

Abstract Background Rising healthcare costs worldwide have spurred interest in using machine learning to pinpoint major healthcare utilizers for population health efforts. Previous studies have focused on individuals who impose the highest financial burden, such as targeting high-need high-cost utilizers, a small group with limited potential for cost reduction. Therefore, we developed models to predict future healthcare utilization across various thresholds. Methods We leveraged data from a multi-institutional diabetes database to develop binary classification models capable of predicting healthcare utilization in the following year. The models predicted six different outcomes: patients with an annual total length of stay of ≥ 7, ≥14, and ≥30 days, and emergency department (ED) attendance of ≥ 3, ≥5, and ≥10 visits. To mitigate class imbalance, we applied random and synthetic minority oversampling techniques and compared against models trained without oversampling. Models were trained with 2019 data and tested on unseen data from 2020 and 2021. Results Models trained with random oversampling, including logistic regression, multivariate adaptive regression splines, boosted trees, and multilayer perceptron, consistently demonstrated high AUC (>0.80) and sensitivity (>0.60) across both training-validation and test datasets. Addressing class imbalance was found to be crucial, as models without oversampling displayed satisfactory AUC (>0.80) but significantly lower sensitivity (<0.10). Key predictors identified included age, number of ED visits in the present year, chronic kidney disease stage, inpatient bed days in the present year, and mean HbA1c levels. Conclusions Our machine learning models successfully predict high service level utilization with strong performance, offering valuable tools for policymakers and health planners to develop targeted health initiatives based on patient utilization patterns. Key messages • Machine learning models can be leveraged to predict future healthcare utilization and facilitate interventional program development. • Annual total length of stay and total emergency department visits are useful service level indicators.

Sustainable lignin-based high-efficiency flame-retardant epoxy resins with excellent mechanical properties

As an abundant natural aromatic polymer, how to realize the high-value utilization of lignin (LA) is still a difficult problem. Herein, a sustainable flame retardant (LA@APP) was synthesized through hydrogen bonding interactions between LA and ammonium polyphosphate (APP), then flame retarded alone epoxy resin (EP/LA@APP). With loading of 6 wt% LA@APP, the LOI of EP/LA@APP6 rose to 27.9 % and achieved UL-94 V0 rating. Meanwhile, EP/LA@APP6 presented a 63.5 % reduction in peak of heat release rate (pHRR) and an 51.3 % decrease in peak of smoke production rate (pSPR). LA@APP could promote the EP composites to form the denser and more continuous expanded charring residuals than APP during the combustion process, effectively shielding the underlying epoxy substrate and thus enhancing fire safety. Additionally, the shell layer of LA@APP, abundant in hydroxyl groups and methoxy groups, effectively improved the compatibility and interfacial interaction with EP. Therefore, the tensile, flexural and impact strength of EP/LA@APP6 reached 50.8 MPa, 86.1 MPa and 7.81 kJ/m2, higher than those of pure EP at 48.3 MPa, 81.5 MPa and 7.45 kJ/m2. This flame retardant with simultaneously enhanced fire safety and mechanical properties of epoxy resin was expected to realize the high-level utilization of lignin.

Machine Learning-Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation.

The cost of health care in many countries is increasing rapidly. There is a growing interest in using machine learning for predicting high health care utilizers for population health initiatives. Previous studies have focused on individuals who contribute to the highest financial burden. However, this group is small and represents a limited opportunity for long-term cost reduction. We developed a collection of models that predict future health care utilization at various thresholds. We utilized data from a multi-institutional diabetes database from the year 2019 to develop binary classification models. These models predict health care utilization in the subsequent year across 6 different outcomes: patients having a length of stay of ≥7, ≥14, and ≥30 days and emergency department attendance of ≥3, ≥5, and ≥10 visits. To address class imbalance, random and synthetic minority oversampling techniques were employed. The models were then applied to unseen data from 2020 and 2021 to predict health care utilization in the following year. A portfolio of performance metrics, with priority on area under the receiver operating characteristic curve, sensitivity, and positive predictive value, was used for comparison. Explainability analyses were conducted on the best performing models. When trained with random oversampling, 4 models, that is, logistic regression, multivariate adaptive regression splines, boosted trees, and multilayer perceptron consistently achieved high area under the receiver operating characteristic curve (>0.80) and sensitivity (>0.60) across training-validation and test data sets. Correcting for class imbalance proved critical for model performance. Important predictors for all outcomes included age, number of emergency department visits in the present year, chronic kidney disease stage, inpatient bed days in the present year, and mean hemoglobin A1c levels. Explainability analyses using partial dependence plots demonstrated that for the best performing models, the learned patterns were consistent with real-world knowledge, thereby supporting the validity of the models. We successfully developed machine learning models capable of predicting high service level utilization with strong performance and valid explainability. These models can be integrated into wider diabetes-related population health initiatives.

Hospitalizations and emergency attendance averted by influenza vaccination in Victoria, Australia, 2017 - 2019.

Seasonal influenza epidemics result in high levels of healthcare utilization. Vaccination is an effective strategy to reduce the influenza-related burden of disease. However, reporting vaccine effectiveness does not convey the population impacts of influenza vaccination. We aimed to calculate the burden of influenza-related hospitalizations and emergency department (ED) attendance averted by influenza vaccination in Victoria, Australia, from 2017 to 2019, and associated economic savings. We applied a compartmental model to hospitalizations and ED attendances with influenza-specific, and pneumonia and influenza (P&I) with the International Classification of Diseases, 10th Revision, Australian Modification (ICD-10-AM) diagnostic codes of J09-J11 and J09-J18, respectively. We estimated an annual average of 7657 (120 per 100000 population) hospitalizations and 20560 (322 per 100000 population) ED attendances over the study period, associated with A$85 million hospital expenditure. We estimated that influenza vaccination averted an annual average of 1182 [range: 556 - 2277] hospitalizations and 3286 [range: 1554 - 6257] ED attendances and reduced the demand for healthcare services at the influenza season peak. This equated to approximately A13 [range: A6 - A25] million of savings over the study period. Calculating the burden averted is feasible in Australia and auseful approach to demonstrate the health and economic benefits of influenza vaccination.

KONDISI SOSIAL BUDAYA DAN EKONOMI MASYARAKAT YANG MEMANFAATKAN KAWASAN EKOSISTEM MANGROVE DI KAMPUNG BUKISI KABUPATEN JAYAPURA

Background : Mangroves are plants whose natural habitat is in coastal areas, so they absolutely need the role of coastal communities, who need empowerment in the implementation of mangrove management. Therefore, the sustainability of coastal areas will be maintained because of conservation activities, as well as they can obtain socio-economic benefits. Along with the increase in population and the high needs of the community, the high level of utilization of mangrove resources has resulted in a high level of utilization of mangrove resources. On the other hand, damaged natural resources, because they are not taken care of by the surrounding community, and are not taken care of by the surrounding community, can hardly meet the needs of the community in Bukisi Village. The purpose of the research is to examine the social, cultural and economic conditions of the people living around the mangrove ecosystem area in Bukisi Village, Yokari District, Jayapura Regency. Method: The research methods used were qualitative and quantitative descriptive methods as well as an overview of social, cultural and economic conditions in the utilization of mangrove ecosystems in Bukisi Village and 35 communities who were used as respondents. Results: Respondents were dominated by men, productive age 26-35 years, predominantly elementary school educated, fishermen, with an income of IDR 1,500,000 - IDR 2,500,000. Socio-economic characteristics illustrate that respondents who utilize the mangrove ecosystem are residents who live around the area and there are sacred places around the mangrove ecosystem area, so that no one is allowed to carry out activities in sacred locations. People in Bukisi village often use the mangrove ecosystem area to look for fish, shellfish, shrimp, crabs and even for firewood. Conclusion: Sustainability management strategies that are prioritized in terms of social, cultural and economic conditions as well as the role of stakeholders who are the most important actors in mangrove forest management in Bukisi Village, Yokari District, Jayapura Regency.

Two-stage robust optimization for optimal operation of hybrid hydrogen–electricity storage system considering ladder-type carbon trading

The prominent problems of renewable energy curtailment and its uncertainty have become a hot topic. To the end, with consideration of environmental friendliness, energy utilization efficiency and operation cost, this paper proposes a hybrid hydrogen–electricity storage system (HHES) operation framework comprising assorted types of coupling devices and carbon capture system (CCS) under ladder-type carbon trading (LCT) mechanism. By considering energy balance constraints, equipments’ operation constraints and erratic rates of RESs and multiple loads, a scheduling model with source-load uncertainties is developed with the goal of minimizing the system’s operation cost and carbon emission. Then, a two-stage tri-level robust model is built to find the optimal scheduling plan under multiple uncertainties given the various sources and loads. The first level is to make an optimal dispatch plan and the second level is to find the worst scenario under the given uncertainty set. The third level aims at enhancing renewable energy accommodation rate and reducing load shedding rate in the worst case. Since the model is formed as a tri-level mixed integer optimization problem, the Column and Constraint Generation (CCG) method is adopted to achieve the optimal result. Finally, numerical analyses are performed. The results show that the proposed approach realizes a flexible dispatch of multiple energy and a high utilization level of multiple storages. Furthermore, this paper studies the influence of uncertainty and uncertainty budget parameters with the comparison of the deterministic model and robust models with different uncertainties. The results show that the proposed HHES has strong robustness and can provide a good reference for energy dispatch.

Assessment of the Level of Utilization of Immunization Services Among Nursing Mothers in Some Local Government Areas of Benue State, Nigeria

The study investigated the level of utilization of immunization services among nursing mothers in some local government areas of Benue state. Descriptive cross sectional research design was adopted and multistage sampling procedure was employed. The questionnaire for the respondents titled assessment of the levels of utilization of immunization services among nursing mothers (ALUISNM). The study population consists of 1,500 nursing mothers in selected LGAs of Benue State. Mean, standard deviation were employed to analyze the data obtained from the respondents, while the null hypothesis was verified using the t-test of ANOVA. It can be deduced that the height of utilization of immunization services amongst the nursing mothers in Benue State is fair. The result that 765 (54.3%) utilized immunization services against 670 (45.7%). The result pointed out that there is high level of utilization of immunization services among women with secondary education (57.6%) followed by those with tertiary education. Based on socio economic status immunization were utilized more by women who are unemployed and self-employed with (56.0%) and (55.3%) respectively. Based on location of immunization services, utilization was more by the urban women (60.0%) than semi urban and rural women. Also based on religion utilization of immunization is higher with those of Islam traditionalist than the Christians. The utilization of immunization services and factors such as socio-economic status [F(2,(1462) = 1.139, p>0.05], location [F(2,(1462) = 2.249, p>0.05]. and religion [F(2,(1462) = 0.528, p>0.05] showed no verifiable significance differences. But there was a significant difference with regards to education [F(3,1461) = 15.773, p<0.05]. Conclusively, there was positive level of utilization of immunization services from the study and therefore recommended that issues like location of health facilities, awareness, education etc should be addressed.

H2Bro – a Novel Decoupled Electrocatalytic-Catalytic Water Splitting Process for Green Hydrogen Production

Green hydrogen produced by water splitting using renewable electricity is essential to achieve net-zero carbon emissions. Present water electrolysis technologies are uncompetitive with low-cost grey hydrogen produced from fossil fuels, limiting their scale-up potential. Disruptive processes that decouple the hydrogen and oxygen evolution reactions and produce them in separate cells or different stages emerge as a prospective route to reduce system cost by enabling operation without expensive membranes and sealing components.An elegant and innovative electrocatalytic-catalytic process was suggested for continuous high-current H2 production via membraneless decoupled water splitting (Schematic 1). H2 as the desired product is evolved on the cathode of the electrolytic compartment in near-neutral conditions, Br- is electrochemically oxidized on the anode side to Br2, which reacts in the bulk of the aqueous electrolyte to form bromate anions (with hypobromous acid and hypobromite anions as the intermediate products), whereas a catalytic transformation of the soluble redox couple (BrO3 -/Br-) mediates O2 evolution in the catalytic compartment.Bromate-mediated decoupled water splitting system offers decoupling of the H2 and O2 gases as a safety measure, a membraneless architecture as a cell design simplification, continuous operation mode at low temperature of 60 °C and less corrosive environment along with the potential use of abundant (electro)catalytic materials. The use of the redox mediator also allows bypassing the energetically unfavorable oxygen evolution reaction and, by doing so, decreasing the cell voltage.The concept of the new electrocatalytic-catalytic system was verified experimentally in batch-to-batch mode, resulting in long-term Faradaic efficiency of 98±2% and voltage efficiency of 62% at 1 A cm-2 already at its infancy stage [1]. Nevertheless, the merging of the electrolytic and catalytic parts into an integrated continuous flow system is still in process.In this work, a variety of possible system part designs were suggested and discussed, with an emphasis on integrating the electrolytic and catalytic parts into a continuous flow system. The first iteration of the laboratory scale experimental setup was designed in a continuous stirred reactor mode, using Pt as the cathode material for HER, DSA as the anode material for BER, and RuO2 Adams powder as the catalyst for bromate decomposition, and aiming for a high bromide utilization (BrO3 -/Br- ratio) level at the cell voltage of 1.6 V and the current density values above 0.2 A cm-2. The system was designed to automatically control the electrochemical parameters of the electrolytic process, to adjust the electrolyte flow rate through the electrolytic cell and the bromate/bromide mixture residence time in the catalytic cell, to control the H2 evolution rate, and to detect undesired O2 evolution (if any) in the electrolytic cell, and to optimize and maintain the temperature and pH values. A set of the key parameters affecting Faradaic efficiency, cell voltage and voltage efficiency are analyzed in detail.Some light was also shed on the development of a new type of solid-buffer PGM-free electrocatalysts for hydrogen evolution reaction (HER) in near-neutral conditions, as well as on deciphering the mechanism of bromate decomposition process and the development of novel PGM-free catalyst materials as more affordable alternatives for RuO2 in the catalytic cell.References Slobodkin, E. Davydova, M. Sananis, A. Breytus, A. Rothschild “Electrochemical and chemical cycle for high-efficiency decoupled water splitting in a near-neutral electrolyte", accepted for publication in Nature Materials Funded by the European Union (ERC), Project #101097966 “Continuous electrolytic-catalytic decoupled water electrolysis for green hydrogen production” (H2Bro). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.” Figure 1

Assessing the factors affecting maple syrup yield in the US and predicting production potential in Kentucky

Maple syrup is an important part of the economy in various regions of the United States. Studies on maple syrup production potential mostly use climatic factors as determinants and, therefore, fail to account for non-climatic factors. In this study, we applied a stochastic production function framework to establish a relationship between maple syrup yield and a set of climatic (temperature and tapping season length) and non-climatic determining factors, such as the number of maple trees and utilization rate of the potential number of taps. Tree characteristics, climatic, and other factors had mixed effects on syrup yield. The number of maple trees, the number of taps, and the minimum temperature had marginal negative effects on average syrup yield, while the length of the season and the maximum temperature had positive effects. A predictive model was developed and used to estimate the potential production of maple syrup under low, medium and high utilization levels in Kentucky, a likely region for maple syrup production. This model could be useful for maple syrup research, education, and extension in maple-producing states.

Impact of eyewear insurance coverage on utilization of eyecare providers in Ontario, Canada

ObjectiveTo obtain prescription eyewear in Ontario, eye exams must be performed by optometrists or ophthalmologists (eye care providers [ECPs]). In 2004, government-insured routine eye exams were delisted for Ontarians aged 20–64 leaving eye exam coverage only for those aged ≤19 and 65+. We assessed whether having eyewear insurance impacts Ontarians’ utilization of ECPs. DesignCross-sectional survey. ParticipantsOntarians aged 12+ without diabetes responding to the Canadian Community Health Survey in 2003, 2005, and 2013/2014. MethodsWe compared the utilization of ECPs by eyewear insurance status and eligibility for government-funded eye exams. Individuals with eyewear insurance funded by employers, government or privately were considered to have insurance. ResultsECP utilization was significantly higher in Ontarians with eyewear insurance versus those without, in all survey years and all age groups, including those eligible for government-funded eye exams (e.g., 66.4% vs 59.1% [p < 0.05] for the 65+). This higher level of utilization was particularly evident among Ontarians aged 20–64 in 2013/2014, when this group no longer had government-funded eye exams (34.9% vs 19.9% among 20–39-year-olds, 43.4% vs 32.9% among 40–64-year-olds, p < 0.05 for both). Adjusting for confounding effects, the likelihood of visiting an ECP was greater among Ontarians with eyewear insurance than those without (adjusted prevalence ratio 1.26 for Ontarians aged 12+ and 1.41 for those aged 20–64; p < 0.05 for both). ConclusionsLack of eyewear insurance negatively impacts the utilization of ECPs, even among Ontarians eligible for government-funded eye exams, where the cost barrier for eye exams has been removed by the Ontario government.

Evaluation of the Impact of Antibiogram Availability and Utilization on Antibiotic Use among Healthcare Providers in Saudi Arabia: A Cross-Sectional Study.

Antibiotic resistance is a significant global health issue, exacerbated by the indiscriminate use of antibiotics without antibiograms. Implementing appropriate stewardship programs that monitor and control antibiotic use is essential to minimize resistance development and ensure optimal patient outcomes. This study aims to assess the impact of antibiogram availability and utilization on antibiotic use among healthcare providers in Saudi Arabia, focusing on whether antibiogram data and utilization influence the appropriateness of antibiotic prescribing practices. A cross-sectional study design was employed, utilizing a self-administered online survey distributed to physicians, pharmacists, and other healthcare providers across various healthcare settings in Saudi Arabia. Data were collected over a 90-day period, from 1 December 2023 to 29 February 2024. Descriptive statistics were used to summarize participants' characteristics, and data were analyzed based on geographical region, participants' positions, and other predetermined differences. Out of 23,860 contacted individuals, 333 responded, and 283 were included in the analysis. The majority (62.2%) reported the availability of antibiograms at their facilities, with 84.1% utilizing them in clinical practice. The frequency of consulting antibiograms varied, with only 21.6% doing so daily. Key barriers to antibiogram availability included lack of expertise, technological infrastructure, and funding. Most participants (68.8%) observed changes in antibiotic prescription practices post-antibiogram introduction, and 86.9% believed antibiograms could shorten patients' length of stay and improve outcomes. However, only 40.9% had received training on antibiogram use. While healthcare providers in Saudi Arabia have a relatively high level of antibiogram availability and utilization, several barriers hinder their consistent use. Enhancing accessibility, promoting frequent use, and providing targeted training could strengthen the impact of antibiograms on antibiotic prescribing practices and antimicrobial stewardship efforts.

HUBUNGAN PEMANFAATAN INTERNET DENGAN PENINGKATAN KAPASITAS PETANI DALAM BERUSAHATANI PADI SAWAH DI DESA LANGGOMEA KECAMATAN UEPAI

The dearth of extension workers in this region impedes farmers' access to information, particularly regarding the advancement of innovations in paddy rice farming. Consequently, farmers are increasingly reliant on the internet, yet only a subset of them is able to utilize it to find information pertinent to farming activities. This study aims to ascertain the utilization of the internet and the capacity of wet-rice farmers in Langgoema Village, Uepai Subdistrict, and to examine the relationship between the two. The population under study consisted of all rice farmers in Langgomea Village, Uepai District, Konawe Regency, amounting to 88 individuals. These farmers were grouped into six farmer groups. The sample size was determined using the Slovin formula with an error rate of 10%, resulting in a sample size of 32 rice farmers. Data were collected using survey techniques, interviews, and documentation using questionnaires. This research employed a quantitative approach. The data were subjected to descriptive statistical analysis using the class interval formula and inferential statistical analysis using the Spearman rank correlation formula. The research findings indicate that the Internet utilization of wetland rice farmers is in the medium category. This can be observed in the Internet utilization indicators, which include information, relationships, and sustainability, all of which are in the moderate category. The capacity of rice paddy farmers is in the medium category. Internet utilization based on knowledge, attitudes, and skills are all in the medium category. The utilization of the Internet by wetland rice farmers in Langgomea Village is found to have a very strong and positive relationship with their capacity. It is evident that high levels of Internet utilization contribute to increased capacity.

JCVI: A versatile toolkit for comparative genomics analysis.

The life cycle of genome builds spans interlocking pillars of assembly, annotation, and comparative genomics to drive biological insights. While tools exist to address each pillar separately, there is a growing need for tools to integrate different pillars of a genome project holistically. For example, comparative approaches can provide quality control of assembly or annotation; genome assembly, in turn, can help to identify artifacts that may complicate the interpretation of genome comparisons. The JCVI library is a versatile Python-based library that offers a suite of tools that excel across these pillars. Featuring a modular design, the JCVI library provides high-level utilities for tasks such as format parsing, graphics generation, and manipulation of genome assemblies and annotations. Supporting genomics algorithms like MCscan and ALLMAPS are widely employed in building genome releases, producing publication-ready figures for quality assessment and evolutionary inference. Developed and maintained collaboratively, the JCVI library emphasizes quality and reusability.

Knowledge, usability and challenges of e-learning platforms for continuing Professional Development of healthcare professionals at University Teaching Hospital of Kigali

BackgroundHealthcare professionals constitute a critical component of clinical care services. To provide the expected service, they must continuously develop their profession through continuous learning. This kind of learning is recognized as continuing professional development (CPD). Traditionally, CPD is offered onsite. Onsite training is associated with some barriers that prevent healthcare professionals from attending such educational activities, including financial difficulties and long distance. This is why online learning is proposed to overcome these barriers.ObjectiveThe main purpose was to evaluate usability, knowledge and challenges of e-learning platforms for CPD of healthcare professionals at University Teaching Hospital of Kigali (CHUK).MethodsThe cross-sectional quantitative study approach was utilized; the data was collected at the workplace of nurses, midwives, and allied health professionals by using a pre-designed questionnaire. The data were analyzed using Statistical Package for the Social Sciences (SPSS) version 25 and presented as frequencies.ResultsA significant majority was aware of CPD e-learning platforms. For example, 95.7% of the participants were familiar with these platforms, indicating that they had some degree of knowledge about their existence and purpose. Regarding the mode of accessing CPD courses, 82.1% of participants preferred online platforms, demonstrating a strong will to use e-learning platforms.ConclusionThis study highlighted a high level of awareness and utilization of CPD e-learning platforms among healthcare professionals at CHUK, additionally, participants expressed confidence in using the platforms but emphasized the need for further support and training.

Overview of the support of geothermal energy investments in the programming period 2014-2020 in Hungary

Geothermal energy has great potential to reduce the greenhouse gas emissions of the energy sector, thus contributing to the realization of EU targets and, of course, those ones set for Hungary. The country has excellent geological and geographical conditions, well known due to the existing databases as well as the know-how to guarantee a high-level utilization of this renewable energy source. Still, only a small part of the estimated potential is utilized and the previous programming period, 2014-2020 did not change this situation significantly. On one hand, the poor planning of the Széchenyi 2020 Programme, on the other hand, the lack of risk management measures, discourage investors and provide a competitive advantage for other renewable energy systems over geothermal, especially for biomass. The present paper pro- vides a comprehensive overview of the EU co-financed support of geothermal energy in the period 2014-2020, identifying relevant calls and successful projects. A questionnaire survey among successful applicants is the other pillar of the article. The critical analysis and recom- mendations of the development policy may contribute to a more efficient support of geother- mal energy in the following programming period(s).

Applied English Listening Strategy on Vocational High School Students of Accounting Department

This research came with the problem of the type and level of the metacognitive, cognitive, and socio-affective listening strategy used by the eleventh-grade students at SMK Negeri 1 Baubau during the listening comprehension session of the English subject. These strategies were important for developing language skills and understanding in various contexts. They could be consciously taught and practiced to improve listening comprehension and communication skills. This research aimed to determine the type and level of metacognitive, cognitive, and socio-affective listening strategy used by the eleventh-grade students at SMK Negeri 1 Baubau during the listening comprehension session of English subject. This research applied a descriptive method to reveal the findings related to the listening strategies. Sixty-seven eleventh-grade students of SMP Negeri 1 Baubau were involved as the research sample. The result found that the students applied three types of listening strategies: metacognitive, cognitive, and socio-affective strategy, in which the metacognitive and the socio-affective were more highly used than the cognitive strategy. Furthermore, those three types of listening strategies obtained high levels of utilization. Accordingly, it was concluded that the students frequently used all strategies in learning English listening comprehension. Summarily, it was expected that by the high use of listening strategy, they were able to be contained in the instruction activity of listening comprehension to promote the students’ learning comprehension achievement.

Analysis of the potential and level of utilization of skipjack tuna (Katsuwonus pelamis) landed at Sadeng Coastal Fishery Port (PPP), Gunungkidul Yogyakarta, Indonesia

Sadeng waters are one of the distribution areas for skipjack tuna (Katsuwonus pelamis), which can be found almost every month and can even be found every day if the fishing season is right, namely February to March or September to October. Sadeng coastal Fishery Port (PPP) has a role as a center of economic activity and plays a role in developing fishing technology. Capture fisheries businesses, and other activities related to the fisheries and marine sectors. The aim of this research is to examine the potential aspects of K. pelamis resources and analyze the optimal use of K. pelamis resources. By assessing the potential of fisheries resources to determine the Maximum Sustainable Yield (MSY) value, information can be obtained on the level of utilization and level of effort. This research was carried out from February to May 2023 on a purse seiner. Sadeng Waters, Yogyakarta, Indonesia. Tools and materials used include cameras, stationery and questionnaire sheets. Primary data and secondary data collection methods use 4 (four) methods, namely observation, interview, documentation and literature study. The research results show that the level of utilization of K. pelamis in Gunungkidul Regency is experiencing overfishing conditions. The highest utilization rate occurred in 2020 at 132%. The potential fish resources in Gunungkidul Regency are the average production of K. pelamis in 2018 to 2022 of 18,122 tons. Based on analysis (MSY) and (Fopt), K. pelamis resources from 2018 to 2022 have reached over fishing. The high level of utilization of K. pelamis occurs due to a lack of supervision from the relevant agencies regarding the recording of production results from fishing vessels based at PPP Sadeng.

Sociodemographic Correlates of Integrated Maternal, Newborn, and Child Health Services Utilization Among Childbearing Mothers in Nigeria.

Background Some social and demographic factors might limit the ability of childbearing mothers (CBMs) to use healthcare services for themselves and their children, thereby exposing them to maternal, infant, and child morbidity and mortality. This study aimed to investigate the sociodemographic correlates of the utilization of integrated maternal, newborn, and child health (IMNCH) services by CBMs in Benue State, Nigeria. Methodology A community-based, correlational survey study was conducted among a random sample of 1,200 CBMs. Face-to-face interviews were conducted using a pretested, structured questionnaire, the Integrated Maternal, Newborn, and Child Health Services Utilization Questionnaire (IMNCHSUQ), and study participants were chosen using a multistage sampling procedure. Of the IMNCHSUQ issued, only 896 copies had complete data, which were used for the analysis. The data collected were analyzed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA). The data were evaluated using the mean, standard deviation, and point-biserial correlation, and the null hypotheses were tested using linear regression at the 0.05 level of significance. Results The majority of the CBMs were married (79.7%) and unemployed (66.0%) while the predominant age group was between 15 and 24 years (42.7%). CBMs had a high level of utilization of IMNCH services (x= 3.30, SD = 0.94). The sociodemographic factors studied had little or no influence on the utilization of IMNCH services among CBMs. The most significant demographic predictor was the place of residence. Marital status, educational level, parity, and age were also found to be significant predictors in a few services. Conclusions Uneven distribution of IMNCH centers may be a major cause of the failure of CBM's lack of utilization of IMNCH services, especially in rural settlements with sparse distribution of facilities.

Use of an Opt-Out vs Opt-In Strategy Increases Use of Residency Mental Health Services.

Background Residents report high levels of distress but low utilization of mental health services. Prior research has shown several barriers that prevent residents from opting into available mental health services. Objective To determine the impact of a mental health initiative centered around an opt-out versus an opt-in approach to help-seeking, on the use of psychotherapy. Methods Resident use of psychotherapy was compared between 2 time frames. During the first time frame (July 1, 2020 to January 31, 2021), residents were offered access to therapy that they could self-initiate by calling to schedule an appointment (opt-in). The second time frame (February 1, 2021 to April 30, 2021) involved the switch to an opt-out structure, during which the same residents were scheduled for a session but could choose to cancel. Additional changes were implemented to reduce stigma and minimize barriers. The outcome was psychotherapy use by residents. Results Of the 114 residents, 7 (6%) self-initiated therapy during the opt-in period. When these same residents were placed in an opt-out context, 59 of the remaining 107 residents (55%) kept their initial appointment, and 23 (39%) self-initiated additional sessions. Altogether, across both phases, a total of 30 of the 114 residents initiated therapy (ie, 7 during the opt-in and 23 during the opt-out). The differences in therapy use between the 2 phases are statistically significant (P<.001 by McNemar's test). Conclusions There was a substantial increase in residents' use of psychotherapy after the opt-out initiative that included efforts to reduce stigma and encourage mental health services.