Cost management analysis of Objective Structured Clinical Examination (OSCE): guide to the universities of medical sciences.

BACKGROUND: The Objective Structured Clinical Examination (OSCE) is a crucial assessment tool for evaluating learners' ability to apply theoretical knowledge in practical clinical situations. It is widely accepted by both students and educators, and the costs associated with conducting OSCE assessments vary depending on the field of study, how it is implemented, staffing needs, standardized patients, and duration of the examination. This study examines the expenses related to administering OSCEs in medical universities. METHOD: Conducted from June to September 2023; this mixed-method study elucidated the cost intricacies of executing a two-day OSCE with 14 eight-minute stations for 100 nursing students. This process unfolds in two phases: a qualitative segment comprising text reviews and 45-minute in-depth interviews with faculty members and OSCE experts, leading to the development of a validated checklist, followed by a quantitative phase in which the tool was distributed to 25 faculty members and 5 specialists for completion. RESULT: The examination costs were delineated into three primary components: time, human resources, equipment, consumables, and necessary supplies. In 2023, the total implementation cost of the OSCE for 100 students across 14 clinical stations was $1028.07, with an estimated per-learner cost of $37.50. Human resources incurred the highest expenditure ($1649.37); while supply costs were relatively lower ($1072.17). Educational infrastructure expenses were excluded because the study focused on the Nursing and Midwifery Faculty's Clinical Skills Center. CONCLUSION: Various factors influence OSCE costs, including national production capabilities of medical supplies, institutional credibility, governance status, examination frequency, student demographics, assessor composition, station count, course content, and examinee volume. The insights derived from this comprehensive examination are significant as entry benchmarks for healthcare systems and higher-level academic evaluations. Understanding OSCE cost dynamics facilitates resource optimization and assessment strategy refinement, thereby improving medical education efficacy.

A statistical analysis method for probability distributions in Erdös-Rényi random networks with preferential cutting-rewiring operation.

The study of specific physiological processes from the perspective of network physiology has gained recent attention. Modeling the global information integration among the separated functionalized modules in structural and functional brain networks is a central problem. In this article, the preferentially cutting-rewiring operation (PCRO) is introduced to approximatively describe the above physiological process, which consists of the cutting procedure and the rewiring procedure with specific preferential constraints. By applying the PCRO on the classical Erdös-Rényi random network (ERRN), three types of isolated nodes are generated, based on which the common leaves (CLs) are formed between the two hubs. This makes the initially homogeneous ERRN experience drastic changes and become heterogeneous. Importantly, a statistical analysis method is proposed to theoretically analyze the statistical properties of an ERRN with a PCRO. Specifically, the probability distributions of these three types of isolated nodes are derived, based on which the probability distribution of the CLs can be obtained easily. Furthermore, the validity and universality of our statistical analysis method have been confirmed in numerical experiments. Our contributions may shed light on a new perspective in the interdisciplinary field of complexity science and biological science and would be of great and general interest to network physiology.

Participatory logic model for a precision child and youth mental health start-up: scoping review, case study, and lessons learned.

Background: The precision child and youth mental health (PCYMH) paradigm has great potential to transform CYMH care and research, but there are numerous concerns about feasibility, sustainablity, and equity. Implementation science and evaluation methodology, particularly participatory logic models created with stakeholders, may help catalyze PCYMH-driven system transformation. This paper aims to: (1) report results of a PCYMH logic model scoping review; (2) present a case study illustrating creation of a participatory logic model for a PCYMH start-up; and (3) share the final model plus lessons learned. Methods: Phase 1: Preparation for the logic model comprised several steps to develop a preliminary draft: scoping review of PCYMH logic models; two literature reviews (PCYMH and implementation science research); an environmental scan of our organization's PCYMH research; a gap analysis of our technological capability to support PCYMH research; and 57 stakeholder interviews assessing PCYMH perspectives and readiness. Phase 2: Participatory creation of the logic model integrated Phase 1 information into a draft from which the final logic model was completed through iterative stakeholder co-creation. Results: Phase 1: The scoping review identified 0 documents. The PCYMH literature review informed our Problem and Impact Statements. Reviewing implementation and evaluation literature resulted in selection of the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) and Behavior Change Wheel (BCW) frameworks to guide model development. Only 1.2% (5/414) of the organization's research projects involved PCYMH. Three technological infrastructure gaps were identified as barriers to developing PCYMH research. Stakeholder readiness interviews identified three themes that were incorporated into the draft. Phase 2: Eight co-creation cycles with 36 stakeholders representing 13 groups and a consensus decision-making process were used to produce the final participatory logic model. Conclusions: This is the first study to report the development of a participatory logic model for a PCYMH program, detailing involvement of stakeholders from initial planning stages to the final consensus-based product. We learned that creating a participatory logic model is time- and labour-intensive and requires a multi-disciplinary team, but the process produced stakeholder-program relationships that enabled us to quickly build and implement the PCYMH start-up. Our processes and final model can inform similar efforts at other sites.

Current oral care practices in an acute aged care setting: An Australian metropolitan hospital perspective.

PURPOSE: To evaluate current oral care practices in an acute aged care hospital setting, and staff perceptions of the barriers and enablers to delivery of evidence-based oral care practices. METHOD: A mixed method study comprised of retrospective file audit and cross-sectional survey was conducted within a single acute aged care unit. Medical records of patients aged ≥ 65 years admitted over a 6 month period were retrospectively audited. A clinician survey was used to explore barriers to and enablers of the delivery of oral care practices using the Capability, Opportunity, Motivation, Behaviour (COM-B) questionnaire. Result were analysed using descriptive statistics. RESULT: Patient file audits (n = 206) found 13.6% (n = 28) of patients had oral care completed, despite 23% (n = 47) of patients being recommended by a speech language pathologist to receive oral care. Staff survey respondents (n = 31) reported they do not have the physical or social opportunities to provide oral care (i.e. adequate resources, time, and social support), however, they were motivated and reported they have the required knowledge and skills to provide oral care. CONCLUSION: There is a need for implementation strategies to enable an interprofessional response to improve the delivery of evidence-based oral care practices and optimise patient outcomes.

Mechanical Unfolding of Network Nodes Drives the Stress Response of Protein-Based Materials.

Biomaterials synthesized from cross-linked folded proteins have untapped potential for biocompatible, resilient, and responsive implementations, but face challenges due to costly molecular refinement and limited understanding of their mechanical response. Under a stress vector, these materials combine the gel-like response of cross-linked networks with the mechanical unfolding and extension of proteins from well-defined 3D structures to unstructured polypeptides. Yet the nanoscale dynamics governing their viscoelastic response remains poorly understood. This lack of understanding is further exacerbated by the fact that the mechanical stability of protein domains depends not only on their structure, but also on the direction of the force vector. To this end, here we propose a coarse-grained network model based on the physical characteristics of polyproteins and combine it with the mechanical unfolding response of protein domains, obtained from single molecule measurements and steered molecular dynamics simulations, to explain the macroscopic response of protein-based materials to a stress vector. We find that domains are about 10-fold more stable when force is applied along their end-to-end coordinate than along the other tethering geometries that are possible inside the biomaterial. As such, the macroscopic response of protein-based materials is mainly driven by the unfolding of the node-domains and rearrangement of these nodes inside the material. The predictions from our models are then confirmed experimentally using force-clamp rheometry. This model is a critical step toward developing protein-based materials with predictable response and that can enable applications for shape memory and energy storage and dissipation.

Should We Use Clinician Self-Report to Tailor Implementation Strategies? Predicting Use of Youth CBT with Clinician Self-Report Versus Direct Observation.

Developing tailored implementation strategies to increase the use of evidence-based practice (EBP) requires accurate identification of predictors of their use. However, known difficulties with measuring EBP use complicates interpretation of the extant literature. In this proof-of-concept study, we examined whether the same predictors of use of cognitive behavioral therapy (CBT) are identified when CBT use is measured with clinician self-report compared to direct observation. We examined four candidate predictors of CBT use - clinician participation in an EBP training initiative, years of experience, caseload, and employment status - in a sample of 36 clinicians (64% female; 72% White and 28% Black) from 19 community mental health agencies treating youth in greater Philadelphia. CBT use was captured for 100 unique client sessions (M = 2.8 recorded sessions per clinician) through both clinician self-report and direct observation, using parallel measures. We used three-level (client, clinician, and agency) regression models with random intercepts to estimate the relationship between each predictor variable and CBT use in both measures and compared the magnitude and direction of each model across self-report and direct observation using z-tests. There was no alignment for any of the four candidate predictors between predictive relationships identified by self-report compared to those identified by direct observation. The findings in this study extend literature documenting limitations of using clinician self-report to capture clinician behavior and suggest that even the characteristics that predict higher self-reported CBT use do not align with (and often are discordant with) those that predict directly observed CBT use. This raises questions about the utility of relying on self-reported use to inform implementation strategy design.

Citizen science observations shed light on how anthropogenic food sources influence wildlife disease.

Research Highlight: Knutie, S., Bahouth, R., Bertone, M., Webb, C., Mehta, M., Nahom, M., Barta, R., Ghai, S., Love, A., Horan, S., Soldo, A., Cochrane, E., Bartholomew, J., Cowan, E., Bjerke, H., Balenger, S., Butler, M., Cornell, A., Kennedy, A., Rolland, V., Schultz, E., Stanback, M., Taff, C., Albery, G. (2024). Understanding spatiotemporal effects of food supplementation on host-parasite interactions using community-based science. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.14155. Wildlife have become increasingly dependent on anthropogenic food supplementation, resulting in altered nutritional intake and inter- and intraspecific interactions. Subsequently, supplemental feeding can affect both the immunological function of individuals and transmission dynamics among individuals and species. The magnitude of the effect supplemental feeding has on disease is likely to vary across time and space with the nutritional demands of hosts. However, the broad temporal or spatial scale effects of supplementation are poorly understood. Recently, Knutie et al. (2024) introduced their citizen science program, the Nest Parasite Community Project, a broadscale coordinated effort by scientists and the public to monitor box nesting wild birds and their ectoparasites across the eastern United States. The authors amassed an impressive 4-year data set with hundreds of nests spanning the entire US breeding range of Eastern bluebirds (Sialia sialis). In the first study to come from the project, the authors demonstrate that the effects of food supplementation on host-parasite interactions vary across time and space and do not consistently influence host-parasite outcomes, highlighting that host-parasite interactions are often context dependent and influenced by many environmental factors (e.g. weather and habitat quality). The authors also found that supplemental feeding increases host fitness, regardless of parasitism. The study provides strong evidence that citizen science projects can help broaden our understanding of how human food sources influence wildlife disease in various environmental contexts.

Synthesis and application of silver nanoparticles from lemon peel extract and other local plant extracts for detection of "blood" at crime scene.

This study reports the enhancement of luminol's sensitivity for blood detection at a crime scene. Silver nanoparticles (AgNPs) were synthesized from the extracts derived from Citrus limon peels, Aloe vera leaves, Capsicum annuum barks from Salix alba, Crinum asiaticum Linn leaves, and Crinum macowanii bulb using green chemistry procedure. Synthesis of silver nanoparticles followed a green chemistry method utilizing water as a solvent, with the phytochemicals from the extracts acting as stabilizing and reducing agents to reduce Ag(I) to Ag(0). The synthesized AgNPs were characterized using Transmission Electron Microscopy (TEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and Ultraviolet-visible (UV-Vis). XRD revealed that the structural composition of silver nanoparticles (AgNPs) was face-centered cubic crystalline (Fcc). TEM imaging demonstrated the spherical crystalline nature of silver nanoparticles (AgNPs) derived from the plant extracts. These silver nanoparticles (AgNPs) were used to enhance the shelf-life, chemiluminescence intensity, specificity, and sensitivity of luminol. It was observed that the silver nanoparticles synthesized from these plant extracts effectively enhanced the chemiluminescence signal of luminol. Moreover, applying plant extracts in blood detection demonstrated a higher fluorescence when exposed to ultraviolet (UV) light.

Reflection on the landscape of education in the area of exposure modelling.

Exposure models are essential for a range of contexts involving exposure science. The Exposure Models Working Group, established under ISES Europe, identified that to improve model use, suitable training and education is required. However, there is currently no formal educational training programme for exposure modelling in Europe. We present results from an online survey disseminated to the European exposure science community to identify modelling training needs. The questionnaire had three sections: background information about the respondents and interests in exposure science, previous attendance of exposure modelling courses, interest in future training and education in exposure modelling and their coverage and format. A total of 88 survey recipients completed the survey. Most respondents were from governmental organizations (36 %), with 50 % of all respondents having less than 10 years of experience. About two-thirds of the respondents stated that they had previously attended exposure modelling courses. These were mostly focused on specific models, up to one day in duration and took place in-person. Two thirds of respondents expressed interest in attending future exposure modelling training modules, with the most important topics being interpretation of model results and use of models to meet regulatory standards/requirements. Preferred attendance of training courses was virtual or blended. Costs and certificates of attendance seemed less influential when selecting a training course. Our survey suggests that there is a demand for training in exposure modelling and provides insight to inform the development of training modules that are suitable to fulfil the training needs of exposure scientists and practitioners.

Analysis of an expanded admission screening protocol for Candida auris at a New York City hospital.

BACKGROUND: Candida auris (C auris) is an emerging global infectious disease threat, and screening practices for identification of C auris are inconsistent across healthcare facilities. This study describes the utility of expanding a C auris admission screening protocol at an acute care hospital to screen all patients presenting from any skilled nursing facility. METHODS: A retrospective review identified all patients screened on admission for C auris from January 2022 through September 2023. Patients were identified for risk potential, and those deemed high risk were placed on transmission-based precautions while awaiting culture results. RESULTS: Of the 591 patients screened, 14 cases were identified (2.4%). Nine cases presented with tracheostomies or were ventilator-dependent and classified as high risk. Five cases were considered low risk at the time of screening. Eight of these newly identified cases would not be screened under the prior criteria. DISCUSSION: This study's findings support prior studies that patients with tracheostomies or were ventilator-dependent have greater risk for C auris colonization. Adopting an expanded admission screening program has allowed the hospital to detect more cases earlier to prevent nosocomial transmissions. CONCLUSIONS: Healthcare facilities should consider initiating or expanding admission screening programs for C auris based on community prevalence rates of C auris.

Rain Gardens and Rain Gauges: Students Get Hands on With Data While Evaluating Green Infrastructure at Their School.

Two modules were designed for high school science students to investigate the performance of a rain garden installed on school property. The rain garden, a green infrastructure system which allows soil infiltration, was installed to reduce impacts to urban streams and can increase the community's resilience to flooding. By involving students in the analysis of this mitigation strategy, students learned new technical skills, gained varied experiences in collecting and analyzing data, were exposed to new STEM careers, and learned about local issues that impact their community while collaborating with local professionals. In each module, students used their authentic data to address research questions. In the first module, students conducted a land survey and calculated the volumetric capacity of the rain garden. In the second module, students collected rainfall data using rain gauges and analyzed various aspects of rainfall collection. Although these modules were focused on a rain garden already installed on school property, they can be implemented at schools without this mitigation strategy present. The surveying module can easily be applied to measure any land surface feature, and the rain gauge module can be implemented anywhere as it is focused on rainfall collection.

Global Insights and Key Trends in Gut Microbiota Research for Premature Infants: A Bibliometric and Visualization Study.

Background: Premature infants, defined as those born before 37 weeks of gestation, face numerous health challenges due to their underdeveloped systems. One critical aspect of their health is the gut microbiota, which plays a vital role in their immune function and overall development. This study provides a comprehensive bibliometric analysis of research trends, influential contributors, and evolving themes in the study of gut microbiota in premature infants over the past two decades. Methods: We conducted a bibliometric analysis using the Web of Science Core Collection database, covering publications from January 1, 2004, to June 17, 2024. We employed VOSviewer, the R package "bibliometrix", and Citespace for data visualization and analysis, focusing on co-authorship, co-citation, and keyword co-occurrence networks. Results: The temporal analysis revealed a significant increase in research output on gut microbiota in premature infants, particularly in the last decade. Early research primarily focused on characterizing the gut microbiota of premature infants, identifying less diversity and a higher prevalence of pathogenic bacteria compared to full-term infants. Key research themes identified include probiotics, necrotizing enterocolitis (NEC), and breastfeeding. Probiotic studies highlighted the potential of strains like Bifidobacterium and Lactobacillus in reducing NEC and sepsis incidences. Breastfeeding research consistently showed the benefits of human milk in fostering a healthier gut microbiota profile. Co-authorship and co-citation analyses identified key contributors and influential studies, emphasizing strong international collaborations, particularly among researchers from the United States, China, and European countries. Conclusion: This bibliometric analysis underscores the growing recognition of the gut microbiota's crucial role in the health of premature infants. The field has seen significant advancements, particularly in understanding how interventions like probiotics and breastfeeding can modulate gut microbiota to improve health outcomes. Continued research and international collaboration are essential to further unravel the complexities of gut microbiota in premature infants and develop effective therapeutic strategies.

Artificial intelligence and machine learning applications for cultured meat.

Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time-and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. In addition, we have included a survey of datasets relevant to CM research. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.

A superhard incompressible carbon allotrope with deformation-induced transformation to diamond.

Recognizing the indispensability of hard materials in industrial applications, the persistent pursuit of ultra-strong and superhard materials has been a subject of extensive research. Carbon, with its versatile hybridization possibilities, emerges as a promising avenue for the creation of such materials. Herein, based on first-principles calculations, we predict an all-sp 3 hybrid orthorhombic carbon allotrope named C10. It exhibits greater incompressibility along the [010] direction than diamond, demonstrating an extreme hardness with Vickers hardness of up to 72.8 GPa. The Young's modulus of C10 displays anisotropy, closely comparable to diamond along the x axis direction, while maintaining excellent mechanical stability within the range of 100 GPa. Notably, under the influence of shear force, it undergoes transformation into diamond. Functioning as a transparent semiconductor with a wide indirect band gap of 4.55 eV, C10 holds promise as a potential superhard material in the semiconductor industry, especially under extreme conditions.

Challenges and opportunities in the selective degradation of organophosphorus herbicide glyphosate.

The wide and continuous usage of glyphosate in the environment poses a serious threat to biological systems. Besides the accumulation of glyphosate in vivo, a growing body of research has revealed that aminomethylphosphonic acid (AMPA), the main degradation intermediate of glyphosate, has significant environmental and biological influences by inducing chromosome aberration of fish and canceration of human erythrocyte. Therefore, the development of new strategies avoiding the generation of the toxic AMPA intermediate during the full degradation of glyphosate is becoming of high importance. Herein, we provide a mini-review that includes the most recent advances in the selective degradation of glyphosate avoiding the generation of AMPA in the last several years from 2018. The developments of the selective degradation of glyphosate, highlighting its synthesis and selective degradation mechanism, are summarized here. This review intends to attract more attention from researchers toward this area and to emphasize the recent developments of selective degradation of glyphosate in highlighting future challenges.

Aluminum saving and CO2 emission reduction from waste recycling of China's rooftop photovoltaics under carbon neutrality strategy.

Rooftop photovoltaics (RPVs) are crucial for decarbonizing the power sector and achieving carbon neutrality, with expected future capacity increases. The growth of RPVs necessitates substantial aluminum (Al) resources, contributing significantly to carbon dioxide (CO2) emissions from Al production. Given China's bauxite shortage, recycling Al from waste RPV panels presents an effective solution to enhance resource security and mitigate CO2 emissions. We developed a framework to project waste RPV quantities and assess the recycling potential of Al and its impact on CO2 emissions from 2020 to 2060. Our findings indicate potential waste flows of 95-221 million tonnes (Mt) and recycled Al ranging from 5 to 28 Mt, with a primary Al supply gap of 25-43 Mt. Recycling could reduce CO2 emissions by 35-207 Mt over the period. This research underscores the importance of Al resource security and sustainable RPV industry development in China's pursuit of carbon neutrality.

What is needed to address psychosocial hazards in road transport? Using implementation science to understand the gaps.

Work-related psychosocial hazards have a significant influence on the development of musculoskeletal and mental health disorders (MSDs and MHDs). This study used behavioural change theory to guide understanding of barriers and enablers for leaders working in the Heavy Vehicle Road Transport industry, to address workplace psychosocial hazards. The findings will be used to inform design of a future intervention to support leaders to more effectively address these hazards. Principal results and major conclusions: A higher number of barriers than enablers were identified. The most prevalent barriers were inadequate knowledge and interpersonal skills, and environmental context and inadequate resources, and the most prevalent enabler was risk management. The findings extend previous evidence by providing more specific detail regarding requirements for addressing psychosocial hazards. Further, the findings identified there is a need to improve the capability, opportunity and motivation of leaders to enable them to more effectively address psychosocial hazards.

The reporting completeness of observational systematic reviews and meta-analysis in social science could be improved: a cross-sectional survey.

OBJECTIVE: To evaluate and compare the reporting quality of observational systematic reviews and meta-analyses (OSRMA) published in Chinese and English-language social science journals using the Meta-analyses Of Observational Studies in Epidemiology (MOOSE) guideline. STUDY DESIGN AND SETTING: We searched the Social Science Core Index (SSCI) via Web of Science and the Chinese Social Science Core Index (CSSCI) via China National Knowledge Infrastructure (CNKI) databases for OSRMAs published from 2012 to 2022 (before December 2022). The process of literature search, selection, and data extraction were performed double blind. RESULTS: We identified 634 OSRMAs, of which 349 were in English-language journals and 285 in Chinese-language journals. The mean reporting completeness rate was 54.6%, and reviews in English had higher reporting completeness than those in Chinese (55.2% vs. 53.9%). All the reviews reported well on background, method, and conclusion, with average reporting rates of 68.1%, 65.2%, and 88.8%, respectively. However, the reporting of search strategy was ignored, with an average rate of 42.9% for reviews in English and 25.8% for reviews in Chinese, especially on the qualifications of searchers, effort to include all available studies, search-software used, handling unpublished studies, and contact with authors. The reporting transparency of these reviews was statistically significantly influenced by multiple review factors, including the language of journals, year of publication, the number of authors, the reporting guideline followed, and the declaration of funding source. CONCLUSION: OSRMAs in social science demonstrate low reporting quality, especially in Chinese-language journals. We suggest adapting the MOOSE guideline to the social science context and promoting its use among researchers and reviewers in this field.

Designing Effective Hybrid Course Curriculum: A Design Science Approach to Gamification and Student Outcomes Validation.

In the modern educational landscape, the integration of gamification into hybrid learning environments has emerged as a promising approach to enhance student outcomes. However, there remains a lack of comprehensive frameworks for designing gamified hybrid courses and validating their impact on student outcomes. This paper proposes a design science-based approach to gamified course design in hybrid learning contexts. Drawing on the principles of design science research, we developed a framework for designing a gamified hybrid course curriculum that incorporates course content, activities, and assessments based on four elements of gamification (achievement elements, utilitarian value, hedonic benefits, and competition). To validate the effectiveness of our approach, we conducted a study with 294 students enrolled in a hybrid business course that implemented the proposed gamification framework. Our findings indicate that all gamification elements of our proposed gamified hybrid courses positively enhance student engagement, achievement, and satisfaction. Ultimately, this paper not only contributes to the 'gamification in education' literature by providing a comprehensive framework for designing engaging and effective hybrid courses but also proposes a roadmap for the application of design science to embed gamification in business course curriculum design within the context of modern hybrid learning environments.