Yoxas/statistical_literacy huggingface dataset:
Vocational issues affect nurses' family functioning in long term. So the purpose of this study is to investigate the relationship between professional self-concept and perceived organizational support with nurses' family functioning. This descriptive correlational study conducted in hospitals affiliated to Isfahan University of Medical Sciences and 250 nurses who met the inclusion criteria participated in this study. The samples were selected conveniently from December 2022 to August 2022. To collect data, Cowin's professional self-concept questionnaire, Eisenberger's perceived organizational support questionnaire, and Epstein's family function measurement were used. Data collection was done in one step and cross-sectional. Descriptive statistics and inferential methods of data analysis consisted of Pearson's correlation coefficient and multiple linear regression were used. Data were analyzed with statistical package for the social sciences (SPSS) version 22. One of the six dimensions of professional self-concept consisted of staff relations significantly predicted the family function of nurses (P = .004). No significant relationship was found between perceived organizational support and nurses' family function (P = .825). Promoting the healthcare organizations' human climate may improve nurses' wellbeing and hereby their family functioning which could further improve quality of healthcare services. It may be worthwhile to suggest supporting interpersonal and interprofessional relationships to ensure a healthy professional life for the nurses.
Vocational programmes run by teachers in the special needs school context can play a significant role in the vocational development of learners with severe intellectual disability (SID). This study aimed to answer the question 'what are the challenges faced by teachers in the implementation of vocational programmes in selected public special needs schools for learners with SID in the Metropolitan (Metro) District within the City of Cape Town?' The objectives were to describe the challenges as perceived by participants, to highlight common and contrasting challenges in the different schools and to share recommendations on support needed. A qualitative descriptive study was conducted. A combination of purposive and snowball sampling strategies was used to select six teachers from six special needs schools. One-on-one semi-structured interviews with teachers were performed. An interview schedule was used as a tool and all interviews were transcribed and translated into English verbatim. Thematic analysis was applied. The findings showed that teachers encounter inadequate resources, a lack of training, and poor support systems. This study highlights the issues of existing policy and the lack of a mandatory policy on vocational programmes in South Africa. The participants' experiences added to the existing literature by providing valuable insights into the obstacles teachers encounter in this relatively new curriculum. A multifaceted policy framework that is well funded and implemented is much needed to address the challenges identified. The findings may contribute to the development and strengthening of policies on vocational programmes within the South African context.
Voice disorders, such as dysphonia, are common among the general population. These pathologies often remain untreated until they reach a high level of severity. Assisting the detection of voice disorders could facilitate early diagnosis and subsequent treatment. In this study, we address the practical aspects of automatic voice disorders detection (AVDD). In real-world scenarios, data annotated for voice disorders is usually scarce due to various challenges involved in the collection and annotation of such data. However, some relatively large datasets are available for a reduced number of domains. In this context, we propose the use of a combination of out-of-domain and in-domain data for training a deep neural network-based AVDD system, and offer guidance on the minimum amount of in-domain data required to achieve acceptable performance. Further, we propose the use of a cost-based metric, the normalized expected cost (EC), to evaluate performance of AVDD systems in a way that closely reflects the needs of the application. As an added benefit, optimal decisions for the EC can be made in a principled way given by Bayes decision theory. Finally, we argue that for medical applications like AVDD, the categorical decisions need to be accompanied by interpretable scores that reflect the confidence of the system. Even very accurate models often produce scores that are not suited for interpretation. Here, we show that such models can be easily improved by adding a calibration stage-trained with just a few minutes of in-domain data. The outputs of the resulting calibrated system can then better support practitioners in their decision-making process.
Vulnerability to malnutrition is very high with low-income women and their children in rural Balochistan with contributing factors including lack of awareness about proper nutrition, low literacy, scarcity of vegetables and fruit, and low purchasing power of households. The Food and Agriculture Organization's kitchen garden program provides resources to improve nutrition and health knowledge and promote healthy eating practices. The objective of this study was to assess nutrition and health awareness, knowledge, attitudes, behavioural intentions/behaviours (AKAB) of women who attended the kitchen garden program and trainings. A community based cross-sectional survey (N = 209) using a two-stage cluster sampling method was used to select households with survey participants being mothers with children under five years of age. A pretested questionnaire was administered via face-to-face surveys by trained enumerators in two districts of Balochistan province of Pakistan. Nutrition and health AKAB were constructed indices. Chi-square tests compared statistical differences in AKAB by women attending against a control group who did not-attend kitchen garden interventions. Binary logistic regression analyses were performed to assess kitchen garden program outcomes against key AKAB indicators, while adjusting for covariates. Significant differences (p<0.001) were identified between intervention and control groups with women attending kitchen garden being more aware of the components of kitchen garden (65.8% vs 36.8%), and more knowledgeable about causes of illnesses caused by poor nutrition including, iron deficiency anemia, pregnancy, and unborn child health complications, compared to women not attending kitchen gardens program. Logistic regression analysis identified women attending kitchen gardens also had higher odds of being more knowledgeable (OR = 1.59, 95%CI 1.27-1.99, p<0.001), having improved attitudes (OR = 4.86, 95%CI 2.77-8.53, p <0.001), and behavioural intentions/behaviours (OR = 1.98, 95%CI 1.26-3.12, p = 0.003) towards improved nutrition and health. Substantial opportunities exist for achieving improved nutrition and health outcomes with vulnerable groups in Balochistan, through greater participation in kitchen gardens behavioural change programs and interventions. As part of scaling-up efforts, academically rigorous project evaluations should be institutionalized for continuous improvement of nutrition programs to address micronutrient deficiencies in rural communities.
Wastewater-based epidemiology (WBE) and wastewater surveillance have become a valuable complementary data source to collect information on community-wide exposure through the measurement of human biomarkers in influent wastewater (IWW). In WBE, normalization of data with the de facto population that corresponds to a wastewater sample is crucial for a correct interpretation of spatio-temporal trends in exposure and consumption patterns. However, knowledge gaps remain in identifying and validating suitable de facto population biomarkers (PBs) for refinement of WBE back-estimations. WBE studies that apply de facto PBs (including hydrochemical parameters, utility consumption data sources, endo- and exogenous chemicals, biological biomarkers and signalling records) for relative trend analysis and absolute population size estimation were systematically reviewed from three databases (PubMed, Web of Science, SCOPUS) according to the PRISMA guidelines. We included in this review 81 publications that accounted for daily variations in population sizes by applying de facto population normalization. To date, a wide range of PBs have been proposed for de facto population normalization, complicating the comparability of normalized measurements across WBE studies. Additionally, the validation of potential PBs is complicated by the absence of an ideal external validator, magnifying the overall uncertainty for population normalization in WBE. Therefore, this review proposes a conceptual tier-based cross-validation approach for identifying and validating de facto PBs to guide their integration for i) relative trend analysis, and ii) absolute population size estimation. Furthermore, this review also provides a detailed evaluation of the uncertainty observed when comparing different de jure and de facto population estimation approaches. This study shows that their percentual differences can range up to ±200 %, with some exceptions showing even larger variations. This review underscores the need for collaboration among WBE researchers to further streamline the application of de facto population normalization and to evaluate the robustness of different PBs in different socio-demographic communities.
Wastewater-based epidemiology (WBE) has been widely implemented around the world as a complementary tool to conventional surveillance techniques to inform and improve public health responses. Currently, wastewater surveillance programs in the U.S. are evaluating integrated approaches to address public health challenges across multiple domains, including substance abuse. In this work, we demonstrated the potential of online solid-phase extraction coupled with liquid chromatography-high-resolution mass spectrometry to support targeted quantification and nontargeted analysis of psychoactive and lifestyle substances as a step toward understanding the operational feasibility of a statewide wastewater surveillance program for substance use assessment in New York. Target screening confirmed 39 substances in influent samples collected from 10 wastewater treatment plants with varying sewershed characteristics and is anticipated to meet the throughput demands as the statewide program scales up to full capacity. Nontarget screening prioritized additional compounds for identification at three confidence levels, including psychoactive substances, such as opioid analgesics, phenethylamines, and cathinone derivatives. Consumption rates of 12 target substances detected in over 80% of wastewater samples were similar to those reported by previous U.S.-based WBE studies despite the uncertainty associated with back-calculations. For selected substances, the relative bias in consumption estimates was sensitive to variations in monitoring frequency, and factors beyond human excretion (e.g., as indicated by the parent-to-metabolite ratios) might also contribute to their prevalence at the sewershed scale. Overall, our study marks the initial phase of refining analytical workflows and data interpretation in preparation for the incorporation of substance use assessment into the statewide wastewater surveillance program in New York.
Water's unique thermophysical properties and how it mediates aqueous interactions between solutes have long been interpreted in terms of its collective molecular structure. The seminal work of Errington and Debenedetti [Nature2001, 409, 318-321] revealed a striking hierarchy of relationships among the thermodynamic, dynamic, and structural properties of water, motivating many efforts to understand (1) what measures of water structure are connected to different experimentally accessible macroscopic responses and (2) how many such structural metrics are adequate to describe the collective structural behavior of water. Diffusivity constitutes a particularly interesting experimentally accessible equilibrium property to investigate such relationships because advanced NMR techniques allow the measurement of bulk and local water dynamics in nanometer proximity to molecules and interfaces, suggesting the enticing possibility of measuring local diffusivities that report on water structure. Here, we apply statistical learning methods to discover persistent structure-dynamic correlations across a variety of simulated aqueous mixtures, from alcohol-water to polypeptoid-water systems. We investigate a variety of molecular water structure metrics and find that an unsupervised statistical learning algorithm (namely, sequential feature selection) identifies only two or three independent structural metrics that are sufficient to predict water self-diffusivity accurately. Surprisingly, the translational diffusivity of water across all mixed systems studied here is strongly correlated with a measure of tetrahedral order given by water's triplet angle distribution. We also identify a separate small number of structural metrics that well predict an important thermodynamic property, the excess chemical potential of an idealized methane-sized hydrophobe in water. Ultimately, we offer a Bayesian method of inferring water structure by using only structure-dynamics linear regression models with experimental Overhauser dynamic nuclear polarization (ODNP) measurements of water self-diffusivity. This study thus quantifies the relationships among several distinct structural order parameters in water and, through statistical learning, reveals the potential to leverage molecular structure to predict fundamental thermophysical properties. In turn, these findings suggest a framework for solving the inverse problem of inferring water's molecular structure using experimental measurements such as ODNP studies that probe local water properties.
We (1) examined the effects of evaluative labels and visual aids on people's understanding, evaluation, and use of the COVID-19 reproduction number (or "r-number"), (2) examined whether people's perceived susceptibility and (intended) adherence to preventive measures changed after being exposed to the r-number, and (3) explored whether these effects and changes depended on people's numeracy skills. In an online experiment, participants from a large Dutch representative sample (N = 1,168) received information about the COVID-19 r-number displayed on the corona dashboard of the Dutch Ministry of Health, Welfare and Sport. The r-number was either presented with or without a categorical line display (i.e., evaluative label) and with or without an icon-based tree diagram (i.e., visual aid) explaining how the number works. Regarding people's use of the statistic, we measured perceived susceptibility to COVID-19 and adherence (intention) to five preventive measures before and after exposure to the r-number. After exposure, we also measured participants' understanding, perceived usefulness, affective and cognitive evaluation, and objective numeracy. About 56% of participants correctly interpreted the r-number, with highly numerate people having better understanding than less numerate people. Information about the r-number was perceived as more useful when presented with a visual aid. There were no differences across experimental conditions in people's understanding, affective, and cognitive evaluations. Finally, independent of experimental conditions, intention to adhere to preventive measures was higher after seeing the r-number, but only among highly numerate people. Although evaluative labels and visual aids did not facilitate people's understanding and evaluation of the r-number, our results show that the statistic is perceived as useful and may be used to stimulate adherence to preventive measures. Policy makers and public health communicators are advised to clearly explain why they are giving these numbers to - especially - the less numerate people, but also how people could use them for behavior change to combat the spread of virus during a pandemic.
We aimed at investigating the preventive role of exercise intervention during pregnancy, in high-risk women for gestational diabetes mellitus (GDM). We searched PubMed, CENTRAL, and Scopus for randomized controlled trials (RCTs) that evaluated exercise interventions during pregnancy on women at high risk for GDM. Data were combined with random effects models. Between study heterogeneity (Cochran's Q statistic) and the extent of study effects variability [I2 with 95% confidence interval (CI)] were estimated. Sensitivity analyses examined the effect of population, intervention, and study characteristics. We also evaluated the potential for publication bias. Among the 1,508 high-risk women who were analyzed in 9 RCTs, 374 (24.8%) [160 (21.4%) in intervention, and 214 (28.1%) in control group] developed GDM. Women who received exercise intervention during pregnancy were less likely to develop GDM compared to those who followed the standard prenatal care (OR 0.70, 95%CI 0.52, 0.93; P-value 0.02) [Q 10.08, P-value 0.26; I2 21% (95%CI 0, 62%]. Studies with low attrition bias also showed a similar result (OR 0.70, 95%CI 0.51, 0.97; P-value 0.03). A protective effect was also supported when analysis was limited to studies including women with low education level (OR 0.55; 95%CI 0.40, 0.74; P-value 0.0001); studies with exercise intervention duration more than 20 weeks (OR 0.54; 95%CI 0.40, 0.74; P-value 0.0007); and studies with a motivation component in the intervention (OR 0.69, 95%CI 0.50, 0.96; P-value 0.03). We could not exclude large variability in study effects because the upper limit of I2 confidence interval was higher than 50% for all analyses. There was no conclusive evidence for small study effects (P-value 0.31). Our study might support a protective effect of exercise intervention during pregnancy for high-risk women to prevent GDM. The protective result should be corroborated by large, high quality RCTs.
We aimed to assess geographic and physician-level variation for hypofractionated whole-breast irradiation (HF-WBI) use for early-stage breast cancer patients in the United States. We further evaluated the association between HF-WBI use and demographic factors after accounting for these variations. We performed a retrospective study of early-stage breast cancer patients using private employer-sponsored insurance claims from 2008 to 2017. Patients were clustered according to geographic level and by radiation oncologist. Bayesian cross-classified multilevel logistic models were used to examine the geographic heterogeneity and variation of radiation oncologists simultaneously. Intracluster correlation coefficient (ICC) and median odds ratios (MOR) were calculated to quantify the variation at different levels. We also used the cross-classified model to identify patient demographic factors associated with receiving HF-WBI. The study included 79,747 women (74.0%) who received conventionally fractionated whole-breast irradiation (CF-WBI) and 27,999 women (26.0%) who underwent HF-WBI. HF-WBI adoption increased significantly across time (2008-2017). The variation in HF-WBI utilization was attributed mostly to physician-level variability (MOR = 2.59). The variability of HF-WBI utilization across core-based statistical areas (CBSAs) (MOR = 1.55) was found to be the strongest among all geographic classifications. After accounting for variability in both CBSAs and radiation oncologists, age, receiving chemotherapy, and several community-level factors, including distance from home to facility, community education level, and racial composition, were found to be associated with HF-WBI utilization. This study demonstrated geographic and physician-level heterogeneity in the use of HF-WBI among early-stage breast cancer patients. HF-WBI utilization was also found to be associated with patient and community-level characteristics. Given observed physician-level variability, intervention through continuing medical education could help doctors to better understand the advantages of HF-WBI and promote the adoption of HF-WBI in the U.S. Influence of physician-level characteristics on HF-WBI utilization merits further study.
We aimed to assess medical students' use of decision-support medical apps and evaluate their perception of app use. A cross-sectional multi-center observational study was conducted among medical students with and without a medical informatics course as part of their undergraduate medical curriculum. We assessed trust, perceptions, patient impression, reliability, and comfort using an online survey. A total of 439 responses were received. There were significant differences between the two groups when indicating which apps, they trust. Students agreed that using apps enhanced knowledge (91%), saved time (88%), improved patient care (85%), and increased diagnostic accuracy (82%). Students indicated that patients would think that students didn't know what they were doing (63%) or students were fresh out of training (53%) when using apps in the presence of patients. Incorporating medical app usage as part of learning may increase trust and comfort with using medical apps in medical practice.
We aimed to assess the association between meditation practice and cognitive function over time among middle-aged and older adults. We included Health and Retirement Study (HRS) participants assessed for meditation practice in the year 2000 as part of the HRS alternative medicine module (n = 1,160) and were followed up for outcomes over 2000-2016 period. We examined the association between meditation ≥ twice a week vs none/less frequent practice and changes in the outcomes of recall, global cognitive function, and quantitative reasoning using generalized linear regression models. Stratified analyses among persons with/without self-reported baseline depressive symptoms were conducted to assess the link between meditation and cognitive outcomes. Among our full study sample, meditation ≥ twice a week was not significantly associated with total recall [β; 95% CI: -0.97, 0.57; p = 0.61], global cognitive function [β; 95% CI: -1.01, 1.12; p = 0.92], and quantitative reasoning [β; 95% CI: -31.27, 8.32; p = 0.26]. However, among those who did not have self-reported depressive symptoms at baseline, meditation ≥ twice a week was associated with improvement in cognitive outcomes such as total recall [β; 95% CI: 0.03, 0.18; p = 0.01] and global cognitive function [β; 95% CI: 0.05, 0.40; p = 0.01] over time. Frequent meditation practice might have a protective effect on cognitive outcomes over time, but this protection could be limited to those without self-reported baseline depressive symptoms. Future studies could incorporate more precise meditation practice assessment, investigate the effect of meditation on cognitive outcomes over time, and include more rigorous study designs with randomized group assignment. This study is not preregistered.