Yoxas/statistical_literacy huggingface dataset:
The School Vaccination Check Program (SVCP) is a public health measure that aims to achieve high levels of National Immunization Program (NIP) vaccination coverage for children by checking the completion of the vaccination schedule for students when they enter elementary or middle school. Due to the COVID-19 pandemic, the SVCP was stopped in 2020 and 2021, and restarted in June-August 2022. In this study, we examined and quantified the relationship with SVCP and the vaccination uptake by comparing the vaccination coverage of 2021 and 2022. Based on the standard schedule, the vaccination records of DTaP5, IPV4, MMR2 and IJEV4 were evaluated for elementary school students. The Tdap6, IJEV5 and HPV1 were evaluated for the students from middle school. Using a difference-in-difference study design and national level big data, the study compared vaccination coverage as of August 2021 and 2022. The study found that the SVCP was effective in increasing vaccination coverage for targeted vaccinations such as DTaP5, IPV4, MMR2 and IJEV4 for elementary school students, and Tdap6, IJEV5 for middle school students. However, the SVCP did not show a statistically significant effect on increasing vaccination coverage on HPV1 for middle school students. School can play an important role to improve vaccination coverage. Therefore, close collaboration with health and education authority is crucial to accomplish successful vaccination program reducing vaccine preventable disease outbreaks in schools.
The scientific community has reached a consensus on humans' important role as causative agents of climate change; however, branches of society are still sceptical about this. Climate change is a key issue for humanity and only the commitment to change human attitudes and lifestyles, at the global level, can be effective in its mitigation. With this purpose, it is important to convey the right message and prevent misinformation to manipulate people's minds. The present study aims to understand the factors shaping European citizens' thoughts on the causes of climate change. Using data from the European Social Survey 10 collected in 2022, we fitted statistical models using the people's thoughts on causes of climate change (natural, anthropogenic or both) as dependent variables. As independent variables, we used the impact of the media through time spent on news and time spent on the internet, level of education, level of trust in scientists, awareness of online or mobile misinformation and gender. We concluded that the typical European citizen who believes in anthropogenic causes of climate change is a female, is more literate, trusts more in scientists, is younger, spends more time reading the news and has more awareness of misinformation presence in online and mobile communications.
The scope of practice for nurses caring for families has evolved to meet the challenges presented by societal changes and increasing needs. In 2015, early childhood nurses from a Swiss region decided to implement a new model of care to guide their practice. The aim of this study was to explore the changes to early childhood nurses' practices following the implementation of the strengths-based nursing and healthcare (SBNH) approach to care. This study of early childhood nurses' (N = 61) practices used a pre-post intervention design and a mixed-method approach. Nurses' competencies and changes in practice were measured using the Nurse Competence Scale (NCS). The quantitative data were analysed using descriptive statistics, Kruskal Wallis tests and logistic regression. Thematic analysis was used to derive themes from the qualitative data. After the intervention, frequency of competency use increased in all domains of the NCS except the "teaching-coaching" domain; perceived levels of competencies also increased in all areas except "helping role" and "diagnostic functions". Age and length of employment at the current post hindered improvement in the "teaching-coaching" competency. Interviews revealed themes related to the implementation process: "adaptability", "implementation process", "ambivalence" and "engagement to change". Other themes were related to practice changes: "developing a disciplinary identity", "path with families" and "strengths". This study showed that the use of perceived competencies changed over time after the introduction of SBNH into practice. Nurses questioned and adapted their routines based on SBNH. Nurse's vision of care also changed; they felt that their care was congruent with their values. For families, this approach allowed a change of vision with a resource-centred approach. Implementation of models of care such as the SBNH in the early childhood context is innovative, as little research in the literature addresses the early childhood community home-visiting context is still modest. This research underlines the added value of this approach on early childhood nurses' competencies.
The script concordance approach aims at triggering judgments in simulated contexts of uncertainty. Nursing students need to be prepared to manage the uncertainty of clinical practice. The purpose of this article is to describe the theoretical foundation and the pedagogical use of the script concordance approach, as well as to present the current state of nursing evidence on the subject. The script concordance approach includes (1) script concordance testing, which is a quantitative examination that evaluates clinical reasoning; (2) a face-to-face script concordance activity; and (3) a digital educational strategy based on script concordance delivered via an online teaching/learning platform that aims to support clinical reasoning development. Relying on questioning and experts' modeling, the script concordance offers an innovative pedagogical approach that approximates the uncertainty of clinical practice.
The Script Concordance Test (SCT) is increasingly used in postgraduate and undergraduate education as a method of summative clinical assessment. It has been shown to have high validity and reliability but there is little evidence of its use in veterinary education as assessment for learning. This study investigates some students' perceptions of the SCT and its effects on their approaches to learning. Final-year undergraduates of the School of Veterinary Medicine and Science (SVMS) at the University of Nottingham participated in a mixed-methods study after completing three formative SCT assessments. A qualitative, thematic analysis was produced from transcripts of three focus group discussions. The quantitative study was a survey based on the analyses of the qualitative study. Out of 50 students who registered for the study, 18 participated in the focus groups and 28 completed the survey. Clinical experience was regarded as the most useful source of information for answering the SCT. The students also indicated that recall of facts was perceived as useful for multiple-choice questions but least useful for the SCT. Themes identified in the qualitative study related to reliability, acceptability, educational impact, and validity of the SCT. The evidence from this study shows that the SCT has high face validity among veterinary students. They reported that it encouraged them to reflect upon their clinical experience, to participate in discussions of case material, and to adopt a deeper approach to clinical learning. These findings strongly suggest that the SCT is potentially a valuable method for assessing clinical reasoning and enhancing student learning.
The search for valid information was one of the main challenges encountered during the COVID-19 pandemic, which resulted in the development of several online alternatives. To describe the development of a computational solution to interact with users of different levels of digital literacy on topics related to COVID-19 and to map the correlations between user behavior and events and news that occurred throughout the pandemic. CoronaAI, a chatbot based on Google's Dialogflow technology, was developed at a public university in Brazil and made available on WhatsApp. The dataset with users' interactions with the chatbot comprises approximately 7,000 hits recorded throughout eleven months of CoronaAI usage. CoronaAI was widely accessed by users in search of valuable and updated information on COVID-19, including checking the veracity of possible fake news about the spread of cases, deaths, symptoms, tests and protocols, among others. The mapping of users' behavior revealed that as the number of cases and deaths increased and as COVID-19 became closer, users showed a greater need for information applicable to self-care compared to following the statistical data. In addition, they showed that the constant updating of this technology may contribute to public health by enhancing general information on the pandemic and at the individual level by clarifying specific doubts about COVID-19. Our findings reinforce the potential usefulness of chatbot technology to resolve a wide spectrum of citizens' doubts about COVID-19, acting as a cost-effective tool against the parallel pandemic of misinformation and fake news.
The second-generation smoking cessation measures for inpatients in our hospital were provided primarily by physicians. Statistics from January to December 2019 showed a negative trend in the number of inpatient smoking cessation services and health education courses provided. Purpose: In this study, a comprehensive systematic literature review on the application of smoking cessation interventions was conducted with the goal of helping enhance the inpatient quit rate at the author's hospital. The literature on smoking cessation interventions was reviewed, with the findings used to formulate a feasible plan for the implementation of an effective related intervention at our hospital. During the implementation process, the challenges encountered led to the formulation of strategies, including: 'conducting second-generation smoking cessation on-the-job training,' 'revising the referral process for patients taking smoking-cessation medications,' and 'adding patients who do not cease smoking to the referral process.' Data on the number of individuals attempting to quit smoking and the success rate of smoking cessation were collected. The baseline values before project implementation were compared with the values at 12 and 24-months posttest. The number of individuals receiving smoking cessation services increased from 85 people within 12 months to 105 people, and further increased to 125 people by the 24th month. Comparing the 3-month abstinence rates for 2019 and 2020, an increase from 31.36% before project implementation to 42.67% after implementation was observed, indicating a rise of 11.31%. Also, comparing the 6-month abstinence rates between 2019 and 2020, an increase from 27.16% before project implementation to 42.67% after implementation was observed, indicating a rise of 15.51%. The project outcomes calculated in December 2021 show a three-month abstinence rate of 44.40% and a six-month abstinence rate of 41.82%. The nursing interventions for smoking cessation in this project increased the abstinence rate among inpatients. Evidence-based practices, including earching for quality research evidence, utilizing the 7A framework to bridge evidence and clinical differences, and promoting the project using a collaborative cross-team approach, were the main factors contributing to the success of the project. The evidence-based application of smoking cessation strategies highlights the significant role played by nurses in enhancing the quality of care. The findings may serve as a reference for the future development of nursing project solutions.
The security and socioeconomic development of China's border areas are of great significance to the nation and the wider world. Using census, statistical, digital elevation model (DEM) and network data, this paper employs visual analysis to capture population distribution patterns in China's 131 border counties from 1982 to 2010. Multiple stepwise regression is carried out to identify the influencing factors of population dynamics in border regions. The main findings include: China's most heavily populated border areas are primarily in the northeast, northwest, and the Guangxi-Yunnan region, while rapid growth of population is found in western Inner Mongolia, southwest Xinjiang, northwest Tibet, and southern Yunnan. Given the increasingly market-oriented migration mechanism, the national reclamation policy has been no longer effective in population attraction in the new century. Education has significantly lowered and will continuously lower the fertility rate in remote border areas. The factors influencing population growth show a remarkable regional heterogeneity along China's long border.
The self-assembly of complex structures from a set of non-identical building blocks is a hallmark of soft matter and biological systems, including protein complexes, colloidal clusters, and DNA-based assemblies. Predicting the dependence of the equilibrium assembly yield on the concentrations and interaction energies of building blocks is highly challenging, owing to the difficulty of computing the entropic contributions to the free energy of the many structures that compete with the ground state configuration. While these calculations yield well known results for spherically symmetric building blocks, they do not hold when the building blocks have internal rotational degrees of freedom. Here we present an approach for solving this problem that works with arbitrary building blocks, including proteins with known structure and complex colloidal building blocks. Our algorithm combines classical statistical mechanics with recently developed computational tools for automatic differentiation. Automatic differentiation allows efficient evaluation of equilibrium averages over configurations that would otherwise be intractable. We demonstrate the validity of our framework by comparison to molecular dynamics simulations of simple examples, and apply it to calculate the yield curves for known protein complexes and for the assembly of colloidal shells.
The seminar-case learning (SCL) method is a case-oriented teaching model, with teachers and students as the main body of teaching, characterized by communication, interaction, and mutual inspiration. This study explored the effects of the SCL method versus traditional lecture-based learning (LBL) in the statistics curriculum for undergraduate students majoring in preventive medicine. Research questions were: 1) whether the scores of students in the experimental group (the SCL model) were higher than those in the control group (the LBL model); 2) whether the students' satisfaction in the experimental group was better than that in the control group; and 3) whether the self-report benefit of students in the experimental group was better than that in the control group. We conducted a two-armed cluster-randomized education intervention trial in practice teaching of health statistics among undergraduates majoring in preventive medicine. Two administrative classes (classes 1-4 and classes 5-8) were divided into the experimental group and the control group according to the principle of drawing lots. The students in two groups received the same statistical theory course. For the arrangement of statistical practice course, the experimental group adopted the SCL model, and the control group used the LBL model. The teaching effect was evaluated via an examination and an anonymous questionnaire survey. Scores for noun explanation questions in the experimental group showed no statistical significance with that of the control group(U = 2911.0, P = 0.964). The scores of single choice, calculation, and case analysis questions, and the total scores were significantly higher than that of the control group (P < 0.05). Students' satisfaction with arrangements of the practice course in the experimental group (92.41%) was significantly higher than that of in the control group (77.03%), the difference was statistically significant (χ2 = 7.074, P = 0.008). The self-report benefit of students in the experimental group was better than that in the control group (P < 0.05). As an effective method of high-quality education, the SCL model is worthy of further promotion in the practice teaching of preventive medicine.
The Serious Illness Care Programme was developed to promote more, better and earlier serious illness conversations. Conversations about goals and values are associated with improved experiences and outcomes for seriously ill patients. Clinicians' attitudes and beliefs are thought to influence the uptake and performance of serious illness conversations, yet little is known about how clinicians perceive the impact of these conversations on patients. This study aimed to explore physicians' perceptions regarding the impact of serious illness conversations for patients. The Serious Illness Care Programme was implemented as a quality improvement project in two hospitals in Southern Sweden. Focus group evaluation discussions were conducted with 14 physicians and inductive thematic analysis was undertaken. The results revealed that physicians considered potential perils and optimised potential payoffs for patients when engaging in serious illness conversations. Potential perils encompassed inappropriate timing, damaging emotions and shattering hopes. Potential payoffs included reflection time, secure space, and united understandings. Physicians depicted a balance in evaluating the perils and payoffs of serious illness conversations for patients and recognised the interrelation of these possibilities through continual assessment and adjustment.
The severe conditions often experienced in an intensive care unit, combined with poor working conditions, increase stress and therefore decrease job satisfaction. Sustained stress in the workplace leads to the development of burnout, a syndrome characterised by three dimensions: emotional exhaustion, depersonalisation and perceived lack of personal fulfilment. To analyse the relationship between burnout syndrome and job satisfaction among ICU personnel. The PubMed, CINAHL and Scopus databases were used. A systematic review and meta-analysis. The study sample consisted of 18 quantitative primary studies conducted in the last five years. Validated questionnaires were used to assess burnout in ICU healthcare workers, the most commonly used being the Maslach Burnout Inventory. The search equation applied was: "job satisfaction AND nurs* AND burnout AND (ICU OR intensive care units)". The search was performed in October 2022. The search returned 514 results. Only 73 articles met the eligibility criteria. After reading the title and abstract, 20 articles were selected. After reading the full texts, 12 articles remained and after the reverse search, 18 articles were finally selected. The studies reported a 50% prevalence of burnout, all three dimensions of which were heightened by the COVID-19 pandemic. Analysis of the study findings revealed an inverse association between burnout and job satisfaction. Job dissatisfaction of ICU nurses depends on lack of experience, working conditions or working environment among others. ICU nurses with lower job satisfaction have higher levels of burnout. This meta-analysis shows the potential value of job satisfaction on improving health outcomes related to burnout syndrome for nursing professional in Intensive Care Units. Different factors that could increase job satisfaction and consequently protect them from suffering high levels of burnout, such as salary, permanence in the service, mental health care are the responsibility of the hospital supervisor and, finally, of the own Health System. Knowledge of a risk profile based on the factors influencing job dissatisfaction would enable the implementation of effective workplace interventions to reduce or prevent the risk of burnout. Health policies should focus on protecting the worker, so in addition to improving working conditions, it would be interesting to promote coping skills in order to improve the quality of care and patient safety.
The severity of coronavirus disease 2019 (COVID-19) may be measured by interleukin-6 (IL-6) and chest X-rays. Brixia score of the chest radiographs is usually used to monitor COVID-19 patients' lung problems. The aim of this study was to demonstrate the relationship between IL-6 levels and chest radiographs (Brixia score) that represent COVID-19 severity. A retrospective cohort study was conducted among COVID-19 patients who had a chest X-ray and examination of IL-6 levels at H. Adam Malik General Hospital, Medan, Indonesia. A multinomial logistic regression analysis was conducted to evaluate the association between IL-6 levels and the severity of the chest radiograph. A total of 76 COVID-19 patients were included in the study and 39.5% of them were 60-69 years old, with more than half were female (52.6%). A total of 17.1%, 48.7%, and 34.2% had IL-6 level of <7 pg/mL, 7-50 pg/mL and >50 pg/mL, respectively. There were 39.5%, 36.8% and 23.7% of the patients had mild, moderate and severe chest X-rays based on Brixia score, respectively. Statistics analysis revealed that moderate (OR: 1.77; 95% CI: 1.05- 3.32) and severe (OR: 1.33; 95% CI: 1.03-3.35) lung conditions in the chest X-rays were significantly associated with IL-6 levels of 7-50 pg/mL. IL-6 more than 50 pg/mL was associated with severe chest X-ray condition (OR: 1.97; 95% CI: 1.15-3.34). In conclusion, high IL-6 levels significantly reflected COVID-19 severity through chest X-rays in COVID-19 patients.
The short-term prediction of single well production can provide direct data support for timely guiding the optimization and adjustment of oil well production parameters and studying and judging oil well production conditions. In view of the coupling effect of complex factors on the daily output of a single well, a short-term prediction method based on a multi-agent hybrid model is proposed, and a short-term prediction process of single well output is constructed. First, CEEMDAN method is used to decompose and reconstruct the original data set, and the sliding window method is used to compose the data set with the obtained components. Features of components by decomposition are described as feature vectors based on values of fuzzy entropy and autocorrelation coefficient, through which those components are divided into two groups using cluster algorithm for prediction with two sub models. Optimized online sequential extreme learning machine and the deep learning model based on encoder-decoder structure using self-attention are developed as sub models to predict the grouped data, and the final predicted production comes from the sum of prediction values by sub models. The validity of this method for short-term production prediction of single well daily oil production is verified. The statistical value of data deviation and statistical test methods are introduced as the basis for comparative evaluation, and comparative models are used as the reference model to evaluate the prediction effect of the above multi-agent hybrid model. Results indicated that the proposed hybrid model has performed better with MAE value of 0.0935, 0.0694 and 0.0593 in three cases, respectively. By comparison, the short-term prediction method of single well production based on multi-agent hybrid model has considerably improved the statistical value of prediction deviation of selected oil well data in different periods. Through statistical test, the multi-agent hybrid model is superior to the comparative models. Therefore, the short-term prediction method of single well production based on a multi-agent hybrid model can effectively optimize oilfield production parameters and study and judge oil well production conditions.