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Abstract Background While positive school climate is important for students’ well-being and mental health, school personnel may experience challenges in creating a nurturing school climate. School-Wide Positive Behavioral Interventions and Supports (SW-PBIS) has shown positive effects on school climate and children’s prosocial behaviors, but fewer studies have been conducted in a European context. Aim This project aims to investigate the effectiveness of SW-PBIS program for students’ social-emotional skills and academic achievement as well as teachers’ and students’ perceptions of classroom learning environment. Furthermore, the study intends to evaluate how school-level factors mediate or moderate the effects of the intervention. In addition, the study includes a qualitative evaluation of the dynamic interaction processes that occur during program implementation in local school contexts. Methods Data on school- and individual-level measures are collected in intervention and control schools. With regard to challenges in retaining control groups over extended time periods, two waves of recruitment are used. In the first wave, an active control group is used and data are collected during three time points. In the second wave, a wait-list control group will be used and data will be collected during two time points during one school year. Hierarchical regression analyses will be conducted to explore the effects of SW-PBIS on the outcomes of the study. An ethno-methodological approach will be applied to provide a detailed examination of the social interactional and meaning-making practices of different school implementation teams, and the negotiation of normative expectations and rules of conduct in peer-teacher-student interactions in different classrooms. Discussion The study is expected to contribute to knowledge on the effects of the SW-PBIS program and how these effects may be mediated or moderated by school-level factors. Combining quantitative and qualitative methods to explore the significance of school contexts in the implementation of the SW-PBIS program constitutes the strength of the study. The challenge in the study is the extended period of implementation of SW-PBIS, which entails difficulties in retaining a control group over the required time period. Therefore, two waves of recruitment are used, encompassing different procedures of allocation to intervention or control group. Trial registration ClinicalTrials.gov Identifier: NCT06270914 on the 22nd of February, 2024 (retrospectively registered).

Abstract Background Sheba Medical Center (SMC) is the largest hospital in Israel and has been coping with a steady increase in total Emergency Department (ED) visits. Over 140,000 patients arrive at the SMC's ED every year. Of those, 19% are admitted to the medical wards. Some are very short hospitalizations (one night or less). This puts a heavy burden on the medical wards. We aimed to identify the characteristics of short hospitalizations. Methods We retrospectively retrieved data of consecutive adult patients admitted to our hospital during January 1, 2013, to December 31, 2019. We limited the cohort to patients who were admitted to the medical wards. We divided the study group into those with short, those with non-short hospitalization and those who were discharged from the ED. Results Out of 133,126 admissions, 59,994 (45.0%) were hospitalized for short term. Patients in the short hospitalization group were younger and had fewer comorbidities. The highest rate of short hospitalization was recorded during night shifts (58.4%) and the rate of short hospitalization was associated with the ED daily patient load (r = 0.35, p

Education in segregated settings for pupils with intellectual disability (ID) has often been portrayed as a unified form of schooling. There is a risk of providing a simplified picture of these settings and of what pupils with ID need to learn. This is generally stipulated in policy documents, leaving room for staff in school to interpret curricula and form learning environments. The aim of this study is to explore how four different learning environments are constructed in segregated settings where pupils with ID are educated in Sweden. The study was conducted as a multiple case study, using observations and interviews with teachers and pupils in four special classes for pupils with ID (SCIDs). Drawing on curriculum theory, the four learning environments, in the results named as Alpha, Beta, Gamma and Delta class, respectively, appeared to be characterized by different ideas of schooling, labelled as knowledge-mediating, socializing, functional life skills and caring as well as teacher-centred classrooms. The study contributes to a deepened understanding of the complexity of education for pupils with ID in segregated settings.

Abstract This study was designed to assess how different prompt engineering techniques, specifically direct prompts, Chain of Thought (CoT), and a modified CoT approach, influence the ability of GPT-3.5 to answer clinical and calculation-based medical questions, particularly those styled like the USMLE Step 1 exams. To achieve this, we analyzed the responses of GPT-3.5 to two distinct sets of questions: a batch of 1000 questions generated by GPT-4, and another set comprising 95 real USMLE Step 1 questions. These questions spanned a range of medical calculations and clinical scenarios across various fields and difficulty levels. Our analysis revealed that there were no significant differences in the accuracy of GPT-3.5's responses when using direct prompts, CoT, or modified CoT methods. For instance, in the USMLE sample, the success rates were 61.7% for direct prompts, 62.8% for CoT, and 57.4% for modified CoT, with a p-value of 0.734. Similar trends were observed in the responses to GPT-4 generated questions, both clinical and calculation-based, with p-values above 0.05 indicating no significant difference between the prompt types. The conclusion drawn from this study is that the use of CoT prompt engineering does not significantly alter GPT-3.5's effectiveness in handling medical calculations or clinical scenario questions styled like those in USMLE exams. This finding is crucial as it suggests that performance of ChatGPT remains consistent regardless of whether a CoT technique is used instead of direct prompts. This consistency could be instrumental in simplifying the integration of AI tools like ChatGPT into medical education, enabling healthcare professionals to utilize these tools with ease, without the necessity for complex prompt engineering.

Traditional methods for measuring chemical exposure have challenges in terms of obtaining sufficient data; therefore, improved methods for better assessing occupational exposure are needed. One possible approach to mitigate these challenges is to use self-monitoring methods such as sensors, diaries, or biomarkers. In the present study, a self-monitored method for measuring soot exposure, which included real-time air monitoring, a work diary, and the collection of urine samples, was evaluated. To validate the method, exposure measurements during the workday and diary entries were compared with velocities calculated from GPS tracking and the expected polycyclic aromatic hydrocarbon (PAH) metabolite patterns in urine. The method was applied with chimney sweeps, an occupational group at a high risk of many severe health outcomes and for whom effective control measures for reducing exposure are needed. In the study, 20 chimney sweeps followed a self-monitoring protocol for 8 consecutive workdays. Personal exposure to soot was measured as black carbon (BC) using micro-aethalometers. A diary was used to record the work tasks performed, and urine samples were collected and analysed for PAH metabolites. From the expected 160 full day measurements, 146 (91%) BC measurements and 149 (93%) diaries were collected. From the expected 320 urine samples, 304 (95%) were collected. The tasks noted in the diaries overlapped with information obtained from the GPS tracking of the chimney sweeps, which covered 96% of the measurement time. The PAH metabolites in urine increased during the work week. Factors believed to have positively influenced the sample collection and task documentation were the highly motivated participants and the continuous presence of trained occupational hygiene professionals during the planning of the study and throughout the measurement stage, during which they were available to inform, instruct, and address questions. In conclusion, the self-monitored protocol used in this study with chimney sweeps is a valuable and valid method that can be used to collect larger numbers of samples. This is especially valuable for occupations in which the employees are working independently and the exposure is difficult to monitor with traditional occupational hygiene methods.

Background: In several cities of Cameroon, the consumption of soy cheese skewers commonly known as “soybean soya” is becoming very common in the streets. Its consumption is increasing thereby encouraging its local production using household methods. Food contamination from processing to consumption is becoming a serious health concern. Therefore, the purpose of this study was to ascertain the microbiological quality of soy cheese skewers sold in some markets and the Critical Control Points (CCPs) in the production process. Methods: Samples were collected in June and August 2022 from different vendors, stored in sterile plastic laminas and placed in a pack containing ice and taken to the laboratory for the determination of the microbiological quality. In order to determine CCPs based on microbiological hazard, samples of slurry, soymilk, fresh soy cheese, stewed soy cheese, and stewed soy cheese skewers were collected from different producers at different production steps and analyzed for microbial contaminations. Results: Results of samples collected from the markets indicated Total Mesophilic Aerobic Bacteria between 4.87 to 7.79 log Colony Forming Unit (CFU)/g, where 86% of the samples were higher than the normal; yeast and mold counts ranged from 2.84 to 5.91 CFU/g; staphylococci, and total coliforms ranged from 0 to 5.17 and 0 to 2.37 log CFU/g, present in 59 and 72% of samples, respectively. Conclusion: Most commercial soy cheese skewer samples showed poor microbiological quality. To reduce these contaminations, preventive measures should be put in place at CCPs (stewing and packaging on skewers) to ensure the safety of food. DOI: 10.18502/jfqhc.11.1.14993

Abstract Background Writing multiple choice questions (MCQs) for the purpose of medical exams is challenging. It requires extensive medical knowledge, time and effort from medical educators. This systematic review focuses on the application of large language models (LLMs) in generating medical MCQs. Methods The authors searched for studies published up to November 2023. Search terms focused on LLMs generated MCQs for medical examinations. Non-English, out of year range and studies not focusing on AI generated multiple-choice questions were excluded. MEDLINE was used as a search database. Risk of bias was evaluated using a tailored QUADAS-2 tool. Results Overall, eight studies published between April 2023 and October 2023 were included. Six studies used Chat-GPT 3.5, while two employed GPT 4. Five studies showed that LLMs can produce competent questions valid for medical exams. Three studies used LLMs to write medical questions but did not evaluate the validity of the questions. One study conducted a comparative analysis of different models. One other study compared LLM-generated questions with those written by humans. All studies presented faulty questions that were deemed inappropriate for medical exams. Some questions required additional modifications in order to qualify. Conclusions LLMs can be used to write MCQs for medical examinations. However, their limitations cannot be ignored. Further study in this field is essential and more conclusive evidence is needed. Until then, LLMs may serve as a supplementary tool for writing medical examinations. 2 studies were at high risk of bias. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

BackgroundArtificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement. ObjectiveThis study aims to analyze the performance of ChatGPT-3.5 and ChatGPT-4 in addressing complex clinical and ethical dilemmas, and to illustrate their potential role in health care decision-making while comparing seniors’ and residents’ ratings, and specific question types. MethodsA total of 4 specialized physicians formulated 176 real-world clinical questions. A total of 8 senior physicians and residents assessed responses from GPT-3.5 and GPT-4 on a 1-5 scale across 5 categories: accuracy, relevance, clarity, utility, and comprehensiveness. Evaluations were conducted within internal medicine, emergency medicine, and ethics. Comparisons were made globally, between seniors and residents, and across classifications. ResultsBoth GPT models received high mean scores (4.4, SD 0.8 for GPT-4 and 4.1, SD 1.0 for GPT-3.5). GPT-4 outperformed GPT-3.5 across all rating dimensions, with seniors consistently rating responses higher than residents for both models. Specifically, seniors rated GPT-4 as more beneficial and complete (mean 4.6 vs 4.0 and 4.6 vs 4.1, respectively; P

BackgroundWith their unmatched ability to interpret and engage with human language and context, large language models (LLMs) hint at the potential to bridge AI and human cognitive processes. This review explores the current application of LLMs, such as ChatGPT, in the field of psychiatry.MethodsWe followed PRISMA guidelines and searched through PubMed, Embase, Web of Science, and Scopus, up until March 2024.ResultsFrom 771 retrieved articles, we included 16 that directly examine LLMs’ use in psychiatry. LLMs, particularly ChatGPT and GPT-4, showed diverse applications in clinical reasoning, social media, and education within psychiatry. They can assist in diagnosing mental health issues, managing depression, evaluating suicide risk, and supporting education in the field. However, our review also points out their limitations, such as difficulties with complex cases and potential underestimation of suicide risks.ConclusionEarly research in psychiatry reveals LLMs’ versatile applications, from diagnostic support to educational roles. Given the rapid pace of advancement, future investigations are poised to explore the extent to which these models might redefine traditional roles in mental health care.

Purpose: To compare the similarity of answers provided by Generative Pretrained Transformer-4 (GPT-4) with those of a consensus statement on diagnosis, nonoperative management, and Bankart repair in anterior shoulder instability (ASI). Methods: An expert consensus statement on ASI published by Hurley et al. in 2022 was reviewed and questions laid out to the expert panel were extracted. GPT-4, the subscription version of ChatGPT, was queried using the same set of questions. Answers provided by GPT-4 were compared with those of the expert panel and subjectively rated for similarity by 2 experienced shoulder surgeons. GPT-4 was then used to rate the similarity of its own responses to the consensus statement, classifying them as low, medium, or high. Rates of similarity as classified by the shoulder surgeons and GPT-4 were then compared and interobserver reliability calculated using weighted κ scores. Results: The degree of similarity between responses of GPT-4 and the ASI consensus statement, as defined by shoulder surgeons, was high in 25.8%, medium in 45.2%, and low 29% of questions. GPT-4 assessed similarity as high in 48.3%, medium in 41.9%, and low 9.7% of questions. Surgeons and GPT-4 reached consensus on the classification of 18 questions (58.1%) and disagreement on 13 questions (41.9%). Conclusions: The responses generated by artificial intelligence exhibit limited correlation with an expert statement on the diagnosis and treatment of ASI. Clinical Relevance: As the use of artificial intelligence becomes more prevalent, it is important to understand how closely information resembles content produced by human authors.

Gluten intolerance, as well as the scarcity of wheat flour in some parts of the world, has prompted the development of gluten-free bread. Gluten-free bread, on the other hand, results in a low specific volume and to remedy this, the use of hydrocolloids and bases has proved to be very successful. The current study aims to determine the optimal proportions of Triumffeta pentendra gum extract and bicarbonate in the breadmaking of a composite flour based on sour cassava starch, peanut flour, and cowpea flour. A Box Benkhen design was used to achieved this, with the variables being the amount of gum extract, the amount of bicarbonate, and the amount of water. The specific volume and texture properties were evaluated as responses. The specific volume was calculated using standard methods, and the textural properties were determined using a texture analyzer. It appears that the incorporation of gums extract, bicarbonate, and water significantly increased the specific volume. The incorporation rate of gum extract significantly increases the hardness, consistency, and masticability which decreases with the incorporation rate of bicarbonate and water. Cohesion and elasticity, on the other hand, increased with the incorporation rate of bicarbonate and water but decreased with the incorporation of gum extract. The optimal gum extract, bicarbonate, and water proportions are 0.28 g, 1.99 g, and 112.5 ml, respectively. As a result, the specific volume is 1.51cm3/g, the hardness is 38.51(N), the cohesion is 0.88, the consistency is 32.86(N), the elasticity is 5.57(1/L), and the masticability is 162.35(mj). According to this findings, gum extracts and sodium bicarbonate can be used to improve the quality of gluten-free bread made with sour cassava starch, peanut and cowpea flour.

Children with severe disabilities are often excluded from educational opportunities due to inter alia attitudinal barriers and a lack of teacher training. This scoping review paper assessed intervention studies focused on inclusive education for children with severe disabilities. It was found that a total of 13 studies met the review's inclusion criteria. The results revealed a paucity of literature and highlighted five main areas of focus in the available intervention studies. The majority of intervention studies were found to be focused on increasing engagement, reducing behaviours and improving social inclusion as outcomes. There was limited focus on studies focusing on the improvement of academic outcomes.

The combination of a superconductor with a magnetically inhomogeneous material has been established as an efficient mechanism for the generation of long-ranged spin-polarized (spin-triplet) Cooper pairs. Evidence for this mechanism, however, has been demonstrated based on studies done on thin-film multilayers, where the strong bonds existing at the interface between the superconductor and the magnetic material should in principle enhance proximity effects and strengthen any electronic correlations. Here, we fabricate devices based on van der Waals (vdW) stacks of flakes of the NbS_{2} combined with flakes of Cr_{1/3}NbS_{2}, which has a built-in magnetic inhomogeneity due to its helimagnetic spin texture at low temperatures. We find that the critical temperature of these vdW heterostructures is strongly dependent on the magnetic state of Cr_{1/3}NbS_{2}, whose degree of magnetic inhomogeneity can be controlled via an applied magnetic field. Our results demonstrate evidence for the generation of long-ranged spin-triplet pairs across the Cr_{1/3}NbS_{2}/NbS_{2} vdW interface.

IntroductionThe field of vestibular science, encompassing the study of the vestibular system and associated disorders, has experienced notable growth and evolving trends over the past five decades. Here, we explore the changing landscape in vestibular science, focusing on epidemiology, peripheral pathologies, diagnosis methods, treatment, and technological advancements.MethodsPublication data was obtained from the US National Center for Biotechnology Information (NCBI) PubMed database. The analysis included epidemiological, etiological, diagnostic, and treatment-focused studies on peripheral vestibular disorders, with a particular emphasis on changes in topics and trends of publications over time.ResultsOur dataset of 39,238 publications revealed a rising trend in research across all age groups. Etiologically, benign paroxysmal positional vertigo (BPPV) and Meniere’s disease were the most researched conditions, but the prevalence of studies on vestibular migraine showed a marked increase in recent years. Electronystagmography (ENG)/ Videonystagmography (VNG) and Vestibular Evoked Myogenic Potential (VEMP) were the most commonly discussed diagnostic tools, while physiotherapy stood out as the primary treatment modality.ConclusionOur study presents a unique opportunity and point of view, exploring the evolving landscape of vestibular science publications over the past five decades. The analysis underscored the dynamic nature of the field, highlighting shifts in focus and emerging publication trends in diagnosis and treatment over time.

In developing countries, blending the powders of locally utilized edible plants for mucilage extraction can be a way to address the growing demand for more viscous and soluble cost-effective hydrocolloids. This study aimed to identify mucilaginous edible plants exploited in the western region of Cameroon in order to enhance the viscosity and water solubility of their mucilages. A semi-administered questionnaire facilitated a comprehensive ethno-nutritional survey. Powders (200 μm) from mucilaginous plants available were defatted, oven dried, and used to produce crude extracts in a water bath (50 °C, 1 h, 2 cycles), with distilled water (1:10, m/v), followed by triple filtration (60 μm, 25 °C) and double centrifugation (2900 g, 20 min). The apparent viscosity of these crude extracts was recorded (spindle 2#, 600 s−1, 60 rpm) at 25 °C, and compared to a 0.2% (m/v) xanthan gum solution (in distilled water). The best plant powders were blended (5 blends) for use under an alkaline condition (pH 10, 0.025 M) for optimal liquid mucilage production and its corresponding powder named hibisanua, derived by coagulating the liquid mucilage (4 °C, 96% ethyl alcohol), oven drying (45 °C), powdering, and sieving (200 μm). Some rheological parameters were determined. FTIR and SEM analysis were carried out with the mucilage hibisanua. Out of 300 surveyed participants, 120 (40%) from 17 ethnicities use the identified plants. Thirty species were found, spanning 27 genera and 19 botanical families. Hibiscus esculentus (okra) pods, Triumfetta annua (nkui) bark, and Corchorus olitorius (lalo) leaves are commonly used in traditional food processing. However, only okra (9.23 Pa s) and nkui (5.54 Pa s) liquid mucilage viscosities were similar to xanthan, making their defatted powders interesting. The validated plant powder mixture (F2) thus comprised 10% nkui and 90% okra (w/w). Their crude extract of concentration 1.51% (w/v) showed the highest dynamic viscosity (23.05 Pa s), not significantly different (p ≥ 0.05) from the control. The resulting dry mucilage with a yield of 215.8 mg/g F2 had the highest solubility (83.05%) with a specific viscosity of 11.519 Pa s upon a desirability of 0.73. All polysaccharides’ solutions exhibited pseudoplastic characteristics and shear-thinning behaviour. A concentrated solution (1.51%) of hibisanua had a rheologic index (0.742) and viscous relaxation time (69.25 s) comparable to xanthan gum. Its structural analysis revealed functional group and morphological changes, typical of highly physical gelling pectic mucilages. These findings support the formulated plant powder approach and the use of its mucilage in the food and pharmaceutical industries.

Abstract Background Inclusive learning environments are considered as crucial for children’s engagement with learning and participation in school. Partnering for change (P4C) is a collaborative school-based service delivery model where services are provided at three levels of intensity based on children’s needs (class, group-, individual interventions). Interventions in P4C are provided universally to support all children with learning, not only children with special education needs (SEN), and as such are expected to be health-promoting. Aim The aim of the study is to evaluate the effectiveness and cost-effectiveness of P4C as well as school staff members’ and children’s experiences after P4C. Methods In a parallel, non-randomised controlled intervention design, 400 children, aged 6–12 years, and their teachers, will be recruited to either intervention classes, working according to the P4C, or to control classes (allocation ratio 1:1). Data will be collected at baseline, post-intervention (4 months), and 11 months follow-up post baseline. The primary outcome is children’s engagement with learning in school. Secondary outcomes include for example children’s health-related quality of life and wellbeing, occupational performance in school, attendance, and special educational needs. The difference-in-differences method using regression modelling will be applied to evaluate any potential changes following P4C. Focus group interviews focusing on children, and professionals’ experiences will be performed after P4C. A health economic evaluation of P4C will be performed, both in the short term (post intervention) and the long term (11-month follow-up). This study will provide knowledge about the effectiveness of P4C on children’s engagement with learning, mental health, and wellbeing, when creating inclusive learning environments using a combination of class-, group- and individual-level interventions. Trial registration number NCT05435937.

Abstract Background The task of writing multiple choice question examinations for medical students is complex, timely and requires significant efforts from clinical staff and faculty. Applying artificial intelligence algorithms in this field of medical education may be advisable. Methods During March to April 2023, we utilized GPT-4, an OpenAI application, to write a 210 multi choice questions-MCQs examination based on an existing exam template and thoroughly investigated the output by specialist physicians who were blinded to the source of the questions. Algorithm mistakes and inaccuracies, as identified by specialists were classified as stemming from age, gender or geographical insensitivities. Results After inputting a detailed prompt, GPT-4 produced the test rapidly and effectively. Only 1 question (0.5%) was defined as false; 15% of questions necessitated revisions. Errors in the AI-generated questions included: the use of outdated or inaccurate terminology, age-sensitive inaccuracies, gender-sensitive inaccuracies, and geographically sensitive inaccuracies. Questions that were disqualified due to flawed methodology basis included elimination-based questions and questions that did not include elements of integrating knowledge with clinical reasoning. Conclusion GPT-4 can be used as an adjunctive tool in creating multi-choice question medical examinations yet rigorous inspection by specialist physicians remains pivotal.

Abstract The United States Medical Licensing Examination (USMLE) has been a subject of performance study for artificial intelligence (AI) models. However, their performance on questions involving USMLE soft skills remains unexplored. This study aimed to evaluate ChatGPT and GPT-4 on USMLE questions involving communication skills, ethics, empathy, and professionalism. We used 80 USMLE-style questions involving soft skills, taken from the USMLE website and the AMBOSS question bank. A follow-up query was used to assess the models’ consistency. The performance of the AI models was compared to that of previous AMBOSS users. GPT-4 outperformed ChatGPT, correctly answering 90% compared to ChatGPT’s 62.5%. GPT-4 showed more confidence, not revising any responses, while ChatGPT modified its original answers 82.5% of the time. The performance of GPT-4 was higher than that of AMBOSS's past users. Both AI models, notably GPT-4, showed capacity for empathy, indicating AI's potential to meet the complex interpersonal, ethical, and professional demands intrinsic to the practice of medicine.