Yoxas/statistical_literacy huggingface dataset:
Cervical cancer screening rates in Haiti are concerningly low. Access to health-related information and health literacy may be important determinants of engagement in cervical cancer screening. This study explored the relationship between sociodemographics,literacy, and sources of information on cervical cancer screening among Haitian women. A secondary data analysis was conducted using USAID Demographics and Health Survey Haiti household data from 2016-2017. Univariate logistic regressions identified significant predictor covariates measuring sociodemographics and sources of information in cervical cancer screening uptake.Two multivariate logistic regression models with adjusted odds ratios were developed using the significant predictor variables from the univariate analysis. N = 610 women responded to questions pertaining to cervical cancer screening. The first multivariate model evaluating sociodemographics demonstrated an economic background of poorer (aOR = 4.06, 95% CI [1.16,14.27]) and richest (aOR = 19.10 , 95% CI[2.58,141.57]), higher education levels (aOR 7.58 , 95% CI [1.64,34.97]), and having insurance (aOR = 16.40, [95% CI 2.65, 101.42]) were significant predictors of cervical cancer screening. The second model evaluating literacy and sources of information indicated that access to a television (aOR = 4.28, 95% CI [1.21,9.34]), mobile phone ownership (aOR = 4.44, 95% CI [1.00,5.59]), and reading the newspaper (aOR = 3.57, [95% CI 1.10,11.59]) were significant predictors of cervical cancer screening. Diverse health communication initiatives that are adapted for literacy level and that incorporate multimedia components may effective in raising women's cervical cancer knowledge and awareness , and increasing intention and uptake of cervical cancer screening in Haiti.
Change theory has increasingly become an area of scholarship in STEM education. While this area has traditionally been a topic for organizational psychology, business management, communication studies, and higher education, STEM education researchers are increasingly aware of the need to use formal theories to guide change efforts and research. Formal change theory encompasses our current research-based knowledge about how and why change occurs, and therefore, can guide the selection and design of promising interventions. Yet learning about and using theory is challenging because many of us have no formal training in this area and relevant scholarship comes from many different disciplines. Inconsistent terminology creates an additional barrier. Thus, this essay aims to contribute to a common lexicon in STEM higher educational change efforts by clearly distinguishing between formalized change theory, which emerges from research, and a theory of change, which guides the logic of a specific project. We also briefly review the current state of the field regarding the use of formal change theory and provide examples of how change theory has been used in biology education. Lastly, we offer practical guidance for researchers and change agents who wish to more intentionally and effectively use change theory in their work.
Changes in demography in developing countries haves led to new issues among older rural populations, such as self-neglect which is under researched.Self-neglect identified as poor self-care, unsafe living quarters, inadequate medical care and poor utilization of services increase the odds of morbidity and mortality.Methods Our study was conducted in Kaniyambadi, a rural block in Vellore district in the state of Tamil Nadu, India. Ten villages were randomly selected. The study was conducted among people older than 60 years who were selected by random sampling using a computer-generated list. The following assessments were done: (i) A 19-item questionnaire was used to assess self-neglect, (ii) Katz index of daily living to assess functional activity, (iii) Mini Mental State Examination to evaluate cognition, (iv) Geriatric Depression Scale to identify depression, (v) Duke Social Support Index to measure social supports. Clinical data and anthropometric data were also collected. Data were entered into Epidata v3.1. All analyses were performed using SPSS v23.0.Results One hundred fourteen people above 60 years of age participated. The prevalence of self-neglect was 21.1% (95% CI 14.9%-29%); about half of the elderly population (47.38%) refused to seek or follow medical advice. Lower levels of education (OR 3.678, 95% CI 1.017 - 13.301), lower social class (OR 4.455, 95% CI 1.236 - 16.050) and functional impairment (3.643, 95% CI 1.373 - 9.668) were found to be significant factors associated with self-neglect. Though prevalence of comorbidities (70%) and depression (27%) were high, there was no statistical evidence of association with self-neglect.
Changes in economy and dietary guidelines brought a great shock to diet quality and meal behaviors, but if these transformations have extended to minerals intake and their sources was still poorly understood. It is essential to evaluate time trends in minerals intake and their sources to inform policy makers. To investigate trends in minerals intake and their sources among U.S. adults. This analysis used dietary data collected by 24-h recalls from U.S. adults (≥ 20 years) in NHANES (1999-March 2020). Minerals intake, age-adjusted percentage of participants meeting recommendations, and minerals sources were calculated among all participants and by population subgroups in each NHANES survey cycle. Weighted linear or logistic regression models were used to examine the statistical significance of time trends. A total of 48223 U.S. adults were included in this analysis. From 1999 to March 2020, intake of calcium (from 0.94 to 1.02 g/day), magnesium (from 308.07 to 321.85 mg/day), phosphorus (from 1.24 to 1.30 g/day), and sodium (from 3.24 to 3.26 mg/day) from food and beverages (FB) and dietary supplements (DSs) significantly increased, and intake of iron (from 19.17 to 16.38 mg/day), zinc (from 16.45 to 14.19 mg/day), copper (from 1.79 to 1.38 mg/day), and potassium (from 2.65 to 2.50 g/day) from FB + DSs decreased (all FDR < 0.05). Additionally, age-adjusted percentage of participants meeting recommendations for calcium, phosphorus, sodium, and selenium significantly increased, that for iron, potassium, zinc, and copper decreased (all FDR < 0.05). Minerals intake and time trends in minerals intake were highly variable depending on age, gender, race/ethnicity, education, and income. For example, white, higher socioeconomic status participants had a higher minerals intake (e.g. iron, zinc, and copper), but had a greater decrease in minerals intake. Furthermore, the percentage of minerals from milks and DSs decreased, and that from beverages increased. From 1999 to March 2020, both minerals intake and their sources experienced a significant alteration among U.S. adults. Many differences in minerals intake and their food sources across sociodemographic characteristics appeared to narrow over time. Although some improvements were observed, important challenges, such as overconsumption of sodium and underconsumption of potassium, calcium, and magnesium, still remained among U.S. adults.
Changing prerequisites in healthcare leads to the increased complexity of nursing. Since there are no regulations on re-validation of competencies for emergency nurses in Sweden there is sparse knowledge on how nurses develop competencies after registration as nurses (RN). To describe self-reported professional competence after postgraduate education among RNs in emergency care settings. A cross-sectional design and STROBE guidelines were used. The short version of the Nurse Professional Competence Scale was used for data collection and the data were collected before and after postgraduate education, descriptive and comparative statistic was used for analysis. 62 (71%) students participated in the first data collection and an independent group of 31 (48%) students participated in the second data collection. The results showed generally good competencies before entering education and significantly improved competencies after education were found in areas of working independently and reviewing literature for evidence-based nursing Conclusion: The competencies were assessed as very good after education. Evaluating nurses' competencies supports educators in developing education to ensure the need for knowledge in emergency care. To ensure required competencies among emergency care nurses there is a need to regulate additional training and re-validation of emergency nurses' competencies.
Characteristics of asthma in the elderly population is not well-known. The aim of the present study was to evaluate asthma in the elderly population, to compare disease characteristics between patients diagnosed <60 (aged asthma) and ≥60 (elderly asthma) years of age. The study was a prospective, multicenter, cross-sectional type. A questionnaire was filled out to patients 60 years of age and over, that have been followed for asthma for at least 3 months. Asthma Control Test (ACT), eight-item Morisky Medication Adherence Scale (MMAS-8) was filled out, inhaler device technique was assessed. A total of 399 patients were included from 17 tertiary care centers across the country. Mean age was 67.11 years and 331 (83%) were female. The age at asthma diagnosis was ≥60 in 146 (36.6%) patients. Patients diagnosed ≥60 years were older (p < 0.001), had higher education level (p < 0.001), more commonly had first-degree relative with asthma (p = 0.038), asthma related comorbidities (p = 0.009) and accompanying rhinitis/rhinosinusitis (p = 0.005), had better asthma control (p = 0.001), were using less controller medications (p = 0.014). Inhaler technique was correct in 37% of the patients with no difference in between the groups. Treatment compliance was better in elderly asthma patients (p < 0.001). In the multivariate logistic regression analysis, having well-controlled asthma (odds ratio = 1.61, CI = 1.04-2.51), and high medication adherence rate (odds ratio = 2.43, CI = 1.48-4.0) were associated with being in the elderly asthma group. The characteristics of asthma are different among patients aged 60 years and over which seems to be related to onset age of asthma. In our cohort, the elderly asthma patients had higher education level, and treatment adherence and asthma control was better. Patients diagnosed ≥60 years of age did not have more severe disease.
Characterization of the uncertainty associated with air emission estimates is of critical importance especially in the compilation of air emission inventories. In this paper, two different theories are discussed and applied to evaluate air emissions uncertainty. In addition to numerical analysis, which is also recommended in the framework of the United Nation Convention on Climate Change guidelines with reference to Monte Carlo and Bootstrap simulation models, fuzzy analysis is also proposed. The methodologies are discussed and applied to an Italian example case study. Air concentration values are measured from two electric power plants: a coal plant, consisting of two boilers and a fuel oil plant, of four boilers; the pollutants considered are sulphur dioxide (SO(2)), nitrogen oxides (NO(X)), carbon monoxide (CO) and particulate matter (PM). Monte Carlo, Bootstrap and fuzzy methods have been applied to estimate uncertainty of these data. Regarding Monte Carlo, the most accurate results apply to Gaussian distributions; a good approximation is also observed for other distributions with almost regular features either positive asymmetrical or negative asymmetrical. Bootstrap, on the other hand, gives a good uncertainty estimation for irregular and asymmetrical distributions. The logic of fuzzy analysis, where data are represented as vague and indefinite in opposition to the traditional conception of neatness, certain classification and exactness of the data, follows a different description. In addition to randomness (stochastic variability) only, fuzzy theory deals with imprecision (vagueness) of data. Fuzzy variance of the data set was calculated; the results cannot be directly compared with empirical data but the overall performance of the theory is analysed. Fuzzy theory may appear more suitable for qualitative reasoning than for a quantitative estimation of uncertainty, but it suits well when little information and few measurements are available and when distributions of data are not properly known.
Characterizing changes in the geographic distribution of neurosurgeons in the United States (US) may inform efforts to provide a more equitable distribution of neurosurgical care. Herein, the authors performed a comprehensive analysis of the geographic movement and distribution of the neurosurgical workforce. A list containing all board-certified neurosurgeons practicing in the US in 2019 was obtained from the American Association of Neurological Surgeons membership database. Chi-square analysis and a post hoc comparison with Bonferroni correction were performed to assess differences in demographics and geographic movement throughout neurosurgeon careers. Three multinomial logistic regression models were performed to further evaluate relationships among training location, current practice location, neurosurgeon characteristics, and academic productivity. The study cohort included 4075 (3830 male, 245 female) neurosurgeons practicing in the US. Seven hundred eighty-one neurosurgeons practice in the Northeast, 810 in the Midwest, 1562 in the South, 906 in the West, and 16 in a US territory. States with the lowest density of neurosurgeons included Vermont and Rhode Island in the Northeast; Arkansas, Hawaii, and Wyoming in the West; North Dakota in the Midwest; and Delaware in the South. Overall, the effect size, as measured by Cramér's V statistic, between training stage and training region is relatively modest at 0.27 (1.0 is complete dependence); this finding was reflected in the similarly modest pseudo R2 values of the multinomial logit models, which ranged from 0.197 to 0.246. Multinomial logistic regression with L1 regularization revealed significant associations between current practice region and residency region, medical school region, age, academic status, sex, or race (p < 0.05). On subanalysis of the academic neurosurgeons, the region of residency training correlated with an advanced degree type in the overall neurosurgeon cohort, with more neurosurgeons than expected holding Doctor of Medicine and Doctor of Philosophy degrees in the West (p = 0.021). Female neurosurgeons were less likely to practice in the South, and neurosurgeons in the South and West had reduced odds of holding academic rather than private positions. The Northeast was the most likely region to contain neurosurgeons who had completed their training in the same locality, particularly among academic neurosurgeons who did their residency in the Northeast.
Characterizing spatial distribution of HIV outcomes is vital for targeting interventions to areas most at risk. We performed spatial analysis to identify geographic clusters and factors associated with mortality in KwaZulu-Natal, South Africa. We utilized Sizanani trial (NCT01188941) data, which enrolled participants August 2010-January 2013 and obtained vital status at 5.8 (IQR 5.0-6.4) years of follow-up. We mapped geocoded addresses to 2011 Census-defined small area layer (SAL) centroids, used Kulldorff's spatial scan statistic to identify mortality clusters, and compared socio-demographic factors for SALs within and outside mortality clusters. We assigned 1,143 participants living with HIV (260 [23%] of whom died during follow-up) to 677 SALs. One lower mortality cluster (n = 90, RR = 0.23, p = 0.022) was identified near a hospital outside Durban. SALs in the cluster were younger (24y vs 25y, p < 0.001); had fewer bedrooms/household (3 vs 4, p < 0.001); had more females (52% vs 51%, p = 0.013) and residents with no schooling past age 20 (4% vs 3%, p < 0.001) or no education at all (4% vs 3%, p < 0.001); had fewer residents with income >3,200 ZAR/month (5% vs 9%, p < 0.001); and had reduced access to piped water (p < 0.001), refuse disposal (p < 0.001), and toilets (p < 0.001). Targeted interventions may improve outcomes in areas with similar characteristics.
Chat generative pretrained transformer (ChatGPT) has the potential to significantly impact how patients acquire medical information online. Here, we characterize the readability and appropriateness of ChatGPT responses to a range of patient questions compared to results from traditional web searches. Patient questions related to the published Clinical Practice Guidelines by the American Academy of Otolaryngology-Head and Neck Surgery were sourced from existing online posts. Questions were categorized using a modified Rothwell classification system into (1) fact, (2) policy, and (3) diagnosis and recommendations. These were queried using ChatGPT and traditional web search. All results were evaluated on readability (Flesch Reading Ease and Flesch-Kinkaid Grade Level) and understandability (Patient Education Materials Assessment Tool). Accuracy was assessed by two blinded clinical evaluators using a three-point ordinal scale. 54 questions were organized into fact (37.0%), policy (37.0%), and diagnosis (25.8%). The average readability for ChatGPT responses was lower than traditional web search (FRE: 42.3 ± 13.1 vs. 55.6 ± 10.5, p < 0.001), while the PEMAT understandability was equivalent (93.8% vs. 93.5%, p = 0.17). ChatGPT scored higher than web search for questions the 'Diagnosis' category (p < 0.01); there was no difference in questions categorized as 'Fact' (p = 0.15) or 'Policy' (p = 0.22). Additional prompting improved ChatGPT response readability (FRE 55.6 ± 13.6, p < 0.01). ChatGPT outperforms web search in answering patient questions related to symptom-based diagnoses and is equivalent in providing medical facts and established policy. Appropriate prompting can further improve readability while maintaining accuracy. Further patient education is needed to relay the benefits and limitations of this technology as a source of medial information.
ChatGPT (OpenAI), a cutting-edge natural language processing model, holds immense promise for revolutionizing medical education. With its remarkable performance in language-related tasks, ChatGPT offers personalized and efficient learning experiences for medical students and doctors. Through training, it enhances clinical reasoning and decision-making skills, leading to improved case analysis and diagnosis. The model facilitates simulated dialogues, intelligent tutoring, and automated question-answering, enabling the practical application of medical knowledge. However, integrating ChatGPT into medical education raises ethical and legal concerns. Safeguarding patient data and adhering to data protection regulations are critical. Transparent communication with students, physicians, and patients is essential to ensure their understanding of the technology's purpose and implications, as well as the potential risks and benefits. Maintaining a balance between personalized learning and face-to-face interactions is crucial to avoid hindering critical thinking and communication skills. Despite challenges, ChatGPT offers transformative opportunities. Integrating it with problem-based learning, team-based learning, and case-based learning methodologies can further enhance medical education. With proper regulation and supervision, ChatGPT can contribute to a well-rounded learning environment, nurturing skilled and knowledgeable medical professionals ready to tackle health care challenges. By emphasizing ethical considerations and human-centric approaches, ChatGPT's potential can be fully harnessed in medical education, benefiting both students and patients alike.
ChatGPT has a wide range of applications in the medical field. Therefore, this review aims to define the key issues and provide a comprehensive view of the literature based on the application of ChatGPT in medicine. This scope follows Arksey and O'Malley's five-stage framework. A comprehensive literature search of publications (30 November 2022 to 16 August 2023) was conducted. Six databases were searched and relevant references were systematically catalogued. Attention was focused on the general characteristics of the articles, their fields of application, and the advantages and disadvantages of using ChatGPT. Descriptive statistics and narrative synthesis methods were used for data analysis. Of the 3426 studies, 247 met the criteria for inclusion in this review. The majority of articles (31.17%) were from the United States. Editorials (43.32%) ranked first, followed by experimental studys (11.74%). The potential applications of ChatGPT in medicine are varied, with the largest number of studies (45.75%) exploring clinical practice, including assisting with clinical decision support and providing disease information and medical advice. This was followed by medical education (27.13%) and scientific research (16.19%). Particularly noteworthy in the discipline statistics were radiology, surgery and dentistry at the top of the list. However, ChatGPT in medicine also faces issues of data privacy, inaccuracy and plagiarism. The application of ChatGPT in medicine focuses on different disciplines and general application scenarios. ChatGPT has a paradoxical nature: it offers significant advantages, but at the same time raises great concerns about its application in healthcare settings. Therefore, it is imperative to develop theoretical frameworks that not only address its widespread use in healthcare but also facilitate a comprehensive assessment. In addition, these frameworks should contribute to the development of strict and effective guidelines and regulatory measures.
ChatGPT is a conversational artificial intelligence (AI) technology that has shown application in various facets of healthcare. With the increased use of AI, it is imperative to assess the accuracy and comprehensibility of AI platforms. This pilot project aimed to assess the understandability, readability, and accuracy of ChatGPT as a source of medication-related patient education as compared with an evidence-based medicine tertiary reference resource, LexiComp®. Patient education materials (PEMs) were obtained from ChatGPT and LexiComp® for eight common medications (albuterol, apixaban, atorvastatin, hydrocodone/acetaminophen, insulin glargine, levofloxacin, omeprazole, and sacubitril/valsartan). PEMs were extracted, blinded, and assessed by two investigators independently. The primary outcome was a comparison of the Patient Education Materials Assessment Tool-printable (PEMAT-P). Secondary outcomes included Flesch reading ease, Flesch Kincaid grade level, percent passive sentences, word count, and accuracy. A 7-item accuracy checklist for each medication was generated by expert consensus among pharmacist investigators, with LexiComp® PEMs serving as the control. PEMAT-P interrater reliability was determined via intraclass correlation coefficient (ICC). Flesch reading ease, Flesch Kincaid grade level, percent passive sentences, and word count were calculated by Microsoft® Word®. Continuous data were assessed using the Student's t-test via SPSS (version 20.0). No difference was found in the PEMAT-P understandability score of PEMs produced by ChatGPT versus LexiComp® [77.9% (11.0) vs. 72.5% (2.4), P=0.193]. Reading level was higher with ChatGPT [8.6 (1.2) vs. 5.6 (0.3), P<0.001). ChatGPT PEMs had a lower percentage of passive sentences and lower word count. The average accuracy score of ChatGPT PEMs was 4.25/7 (61%), with scores ranging from 29-86%. Despite comparable PEMAT-P scores, ChatGPT PEMs did not meet grade level targets. Lower word count and passive text with ChatGPT PEMs could benefit patients, but the variable accuracy scores prevent routine use of ChatGPT to produce medication-related PEMs at this time.