Yoxas/statistical_literacy huggingface dataset:
The positive impacts of work engagement among specialist nurses on retention, organizational commitment, and quality of care are well-documented. However, there is a lack of research on the specific differences in work engagement among specialist nurses. Therefore, the purpose of this study is to assess the level of work engagement among specialist nurses in China and identify its influencing factors. A descriptive cross-sectional study was conducted in China from April to July, 2023, with 724 nurses selected from 22 hospitals through convenience sampling involved. The survey was conducted by using self-administered general information questionnaires and work engagement scales. Questionnaire Star was employed as the online data collection tool. The collected data was analyzed by using descriptive statistics and stepwise regression analysis to draw meaningful conclusions from the study. Among specialist nurses in Xiamen, China, who had a response rate of 97.10%, an average work engagement score is 140.35 (SD=18.17), with the highest score for the work attitude at 4.65 (SD=0.52) and the lowest score for the work recognition at 4.09 (SD=0.85). It was shown through regression analysis that factors such as career satisfaction, involvement in challenging case discussions, marital status, gender, presence of promotion advantage and title accounted for 14.5% of the total variance in the model and were significant explanatory variables that could predict work engagement. It is shown that specialist nurses in Xiamen, China have a high level of work engagement. It is imperative for nursing managers to prioritize the work engagement of specialist nurses, provide the specialist nurses with ample development opportunities and room for growth, and effectively promote the overall development of specialist nurses by improving work engagement in various aspects.
The postpartum period is a critical moment for the delivery of family planning services. However, the utilization of family planning among women in sub-Saharan Africa is not optimal. Therefore, the current study aims to assess the intention to use postpartum contraception and its related determinants in the sub-Saharan African setting. This study utilized a comprehensive search strategy that involved searching several databases, including PubMed, Scopus, EMBASE, Science Direct, Google Scholar, and online research institutional repository homes. Data extraction was performed using Microsoft Excel, and statistical analysis was conducted using STATA software (version 14). To assess publication bias, a forest plot, Begg's rank test, and Egger's regression test were employed. Heterogeneity was evaluated using the I2 statistic, and an overall estimated analysis was conducted. In addition, sensitivity analysis was performed to examine the impact of each study on the overall estimate. Meta-regression analysis was conducted to identify potential sources of heterogeneity. Finally, the pooled odds ratio (OR) for associated factors was calculated. After reviewing 1,321 articles, 14 studies were deemed eligible for inclusion in this meta-analysis. The final analysis comprised a total of 39,936 study participants. The overall intention to use postpartum contraception in sub-Saharan Africa was found to be 62.21% (95% confidence interval [CI]: 55.532-68.875). In subgroup analysis, the highest prevalence of intention was observed in Ethiopia (66.71%; 95% CI: 50.36-83.05), while the lowest prevalence was reported in Ghana (59.39%; 95% CI: 50.22-68.57). The intention to use contraception was found to be 67.22% (95% CI: 62.37-72.07) and 54.53% (95% CI: 46.61-62.45) for institutional and community-based studies, respectively. Maternal educational status (OR = 1.22; 95% CI: 1.09-1.38) and husbands' approval of contraceptive use (OR = 2.395; 95% CI: 1.256-4.567) were identified as predictors of intention to use postpartum contraception. In conclusion, the results of our study show a comparatively low intention toward the use of postpartum contraception, in contrast to findings reported in other countries. As such, we recommend that stakeholders prioritize maternal education and encourage male partner involvement in family planning decisions.
The potential benefits of IT for the well-being of older adults have been widely anticipated. However, findings regarding the impact of internet use on depressive symptoms are inconsistent. As a result of IT's exponential growth, internet skills have supplanted internet access as the source of the digital divide. This study evaluates the effect of internet skills on depressive symptoms through an instrumental variables (IV) approach. Data from the China Health and Retirement Longitudinal Study's wave 4 (2018) were used. This included 16,949 community residents aged 45 years and older. To overcome the endogeneity issue, we used an IV approach. Our results reveal the emergence of a second-level digital divide, the disparity in internet skills, among Chinese middle-aged and older adults. Liner regression suggests that a 1% increase in internet skills is associated with a 0.037% decrease in depressive symptoms (β=-.037, SE 0.009), which underestimates the causal effect. As expected, internet skills are an endogenous variable (F test P value <.001). IV regressions indicate that a 1% increase in internet skills reduces 1.135% (SE 0.471) to 1.741% (SE 0.297) of depressive symptoms. These 2 IV are neither weak (F-1=16.7 and 28.5; both >10) nor endogenous (Wu-Hausman test P value of .10; >.05 or >.01). Better mental health is predicted through improved and higher internet skills. Consequently, residents and policy makers in China should focus on bridging the digital divide in internet skills among middle-aged and older adults.
The potential for exposure to indoor radon varies dramatically across British Columbia (BC) due to varied geology. Individuals may struggle to understand their exposure risk and agencies may struggle to understand the value of population-level programs and policies to mitigate risk. The BC Centre for Disease Control (BCCDC) established the BC Radon Data Repository (BCRDR) to facilitate radon research, public awareness, and action in the province. The BCRDR aggregates indoor radon measurements collected by government agencies, industry professionals and organizations, and research and advocacy groups. Participation was formalized with a data sharing agreement, which outlines how the BCCDC anonymizes and manages the shared data integrated into the BCRDR. The BCRDR currently holds 38,733 measurements from 18 data contributors. The repository continues to grow with new measurements from existing contributors and the addition of new contributors. A prominent use of the BCRDR was to create the online, interactive BC Radon Map, which includes regional concentration summaries, risk interpretation messaging, and health promotion information. Anonymized BCRDR data are also available for external release upon request. The BCCDC leverages existing radon measurement programs to create a large and integrated database with wide geographic coverage. The development and application of the BCRDR informs public health research and action beyond the BCCDC, and the repository can serve as a model for other regional or national initiatives.
The potential for machine learning (ML) to enhance the efficiency of medical specialty boards has not been explored. We applied unsupervised ML to identify archetypes among American Board of Family Medicine (ABFM) Diplomates regarding their practice characteristics and motivations for participating in continuing certification, then examined associations between motivation patterns and key recertification outcomes. Diplomates responding to the 2017 to 2021 ABFM Family Medicine continuing certification examination surveys selected motivations for choosing to continue certification. We used Chi-squared tests to examine difference proportions of Diplomates failing their first recertification examination attempt who endorsed different motivations for maintaining certification. Unsupervised ML techniques were applied to generate clusters of physicians with similar practice characteristics and motivations for recertifying. Controlling for physician demographic variables, we used logistic regression to examine the effect of motivation clusters on recertification examination success and validated the ML clusters by comparison with a previously created classification schema developed by experts. ML clusters largely recapitulated the intrinsic/extrinsic framework devised by experts previously. However, the identified clusters achieved a more equal partitioning of Diplomates into homogenous groups. In both ML and human clusters, physicians with mainly extrinsic or mixed motivations had lower rates of examination failure than those who were intrinsically motivated. This study demonstrates the feasibility of using ML to supplement and enhance human interpretation of board certification data. We discuss implications of this demonstration study for the interaction between specialty boards and physician Diplomates.
The potential impact of large-scale combat operations and multidomain operations against peer adversaries poses significant challenges to the Military Health System including large volumes of critically ill and injured casualties, prolonged care times in austere care contexts, limited movement, contested logistics, and denied communications. These challenges contribute to the probability of higher casualty mortality and risk that casualty care hinders commanders' forward momentum or opportunities for overmatch on the battlefield. Novel technical solutions and associated concepts of operation that fundamentally change the delivery of casualty care are necessary to achieve desired medical outcomes that include maximizing Warfighter battle-readiness, minimizing return-to-duty time, optimizing medical evacuation that clears casualties from the battlefield while minimizing casualty morbidity and mortality, and minimizing resource consumption across the care continuum. These novel solutions promise to "automate" certain aspects of casualty care at the level of the individual caregiver and the system level, to unburden our limited number of providers to do more and make better (data-driven) decisions. In this commentary, we describe concepts of casualty digital twins-virtual representations of a casualty's physical journey through the roles of care-and how they, combined with passive data collection about casualty status, caregiver actions, and real-time resource use, can lead to human-machine teaming and increasing automation of casualty care across the care continuum while maintaining or improving outcomes. Our path to combat casualty care automation starts with mapping and modeling the context of casualty care in realistic environments through passive data collection of large amounts of unstructured data to inform machine learning models. These context-aware models will be matched with patient physiology models to create casualty digital twins that better predict casualty needs and resources required and ultimately inform and accelerate decision-making across the continuum of care. We will draw from the experience of the automotive industry as an exemplar for achieving automation in health care and inculcate automation as a mechanism for optimizing the casualty care survival chain.
The potential of health apps for health promotion and disease prevention is widely recognized. However, uptake is limited due to barriers individuals face in finding suitable and trustworthy apps, such as the overwhelming amount of available health apps. Therefore, the health app platform "FitKnip" was developed, enabling individuals to purchase preselected, trustworthy health apps with a budget of 100 euros (a currency exchange rate of EUR €1=US $1.0831 is applicable). The platform aimed to empower individuals to improve their health and vitality, ultimately supporting a more healthy society. The primary aim of this study was to evaluate the health app platform in terms of feasibility and acceptability. Potential effects on health empowerment and health outcomes were secondarily explored. This quantitative study was part of a mixed methods study with a prospective pre-post interventional design. We collected web-based user data, and self-reported web-based questionnaires were collected over 5 measurements over an 8-month period. Use statistics were tracked on the platform, including the number of purchased apps and euros spent per user registered within the health app platform. We measured the user-friendliness of the health app platform using the System Usability Scale (SUS) and satisfaction using the Client Satisfaction Questionnaire-8 (CSQ-8) and several 10-point Likert items. We asked participants to indicate, on a scale from 1 (not at all) to 10 (completely), how much the health app platform contributed to various areas related to health empowerment. We assessed health-related quality of life by the 12-item Short-Form Health Survey (SF-12) and one's perceived level of stress by the 10-item Perceived Stress Scale (PSS-10). A total of 1650 participants were included, of whom 42% (685/1650) bought at least 1 app. The majority of those purchased one app (244/685, 35.6%). The health app platform was rated as user-friendly (SUS mean 66.5, SD 20.7; range 66.5-70.0), and the acceptability of the health app platform was moderate (CSQ-8 mean 20.0, SD 1.5; range 19.6-20.0). Results furthermore showed that participants were generally satisfied to highly satisfied with the ease of the payment system to purchase apps on the platform (median 8, IQR 7-10), the look and feel of the platform (median 7, IQR 6-8), as well as the provided budget of 100 euros (median 9, IQR 7-10). Participants were less satisfied with the amount (median 6, IQR 4-7) and diversity (median 6, IQR 4-7) of apps offered on the platform. A health app platform is a promising initiative to enhance public health. Feasibility and acceptability are critical for success, as they ensure that such a platform is accessible, user-friendly, and meets end users' needs and preferences. This can help to increase uptake, engagement, and ultimately the platform's adoption and effectiveness.
The Practical Guide to Implementing PROMs in Gender-Affirming Care (PG-PROM-GAC) is an evidence-based resource, which was developed in response to international calls for improved patient-reported outcome measure (PROM) implementation in gender-affirming care. The PG-PROM-GAC has the potential to improve PROM implementation; however, its real-world effectiveness has not yet been investigated. Investigate effectiveness and fidelity of three implementation strategies from the PG-PROM-GAC in a real-world gender clinic setting. Interrupted time series mixed-methods study investigating response rates to a PROM deployed alongside implementation strategies from the PG-PROM-GAC; and open-ended feedback on the fidelity and effectiveness of implementation strategies. Participants were recruited from a National Health Service (NHS) gender clinic. Eligible participants were being seen at an NHS gender clinic for an appointment during the study period, and were invited to participate in this study via email. Three implementation strategies from the PG-PROM-GAC deployed alongside a PROM. Response rates were calculated at 2-week intervals, in line with the deployment of each implementation strategy. Open-ended responses were thematically analysed by two researchers following guidance from implementation science and interpretation from Normalisation Process Theory. A total of 28 participants were included in this study with a mean (SD) age of 39 (17) years. In general, participants rated education material for PROMs as the most important for PROM implementation, and accessibility options for PROMs as the second most important. Response rates to PROM completion dropped as the study progressed, as the burden of reviewing implementation strategies increased. Results were used to construct recommendations for future PROM implementation efforts. The PG-PROM-GAC and implementation strategy materials developed from this study (ie, educational video on PROMs co-developed with key stakeholders) can be used by clinicians, researchers and policymakers to lead PROM implementation efforts in gender-affirming care.
The practice of female genital mutilation is associated with harmful social norms promoting violence against girls and women. Various studies have been conducted to examine the prevalence of female genital mutilation and its associated factors. However, there has been limited studies conducted to assess the association between female genital mutilation and markers of women's autonomy, such as their ability to negotiate for safer sex. In this study, we examined the association between female genital mutilation and women's ability to negotiate for safer sex in sub-Saharan Africa (SSA). We pooled data from the most recent Demographic and Health Surveys (DHS) conducted from 2010 to 2020. Data from a sample of 50,337 currently married and cohabiting women from eleven sub-Saharan African countries were included in the study. A multilevel binary logistic regression analysis was used to examine the association between female genital mutilation and women's ability to refuse sex and ask their partners to use condom. Adjusted odds ratios (aORs) with a 95% confidence interval (CI) were used to present the findings of the logistic regression analysis. Statistical significance was set at p<0.05. Female genital mutilation was performed on 56.1% of women included in our study. The highest and lowest prevalence of female genital mutilation were found among women from Guinea (96.3%) and Togo (6.9%), respectively. We found that women who had undergone female genital mutilation were less likely to refuse sex from their partners (aOR = 0.91, 95% CI = 0.86, 0.96) and ask their partners to use condoms (aOR = 0.82, 95% CI = 0.78, 0.86) compared to those who had not undergone female genital mutilation. Female genital mutilation hinders women's ability to negotiate for safer sex. It is necessary to implement health education and promotion interventions (e.g., decision making skills) that assist women who have experienced female genital mutilation to negotiate for safer sex. These interventions are crucial to enhance sexual health outcomes for these women. Further, strict enforcement of policies and laws aimed at eradicating the practice of female genital mutilation are encouraged to help contribute to the improvement of women's reproductive health.
The precise delineation of esophageal gross tumor volume (GTV) on medical images can promote the radiotherapy effect of esophagus cancer. This work is intended to explore effective learning-based methods to tackle the challenging auto-segmentation problem of esophageal GTV. By employing the progressive hierarchical reasoning mechanism (PHRM), we devised a simple yet effective two-stage deep framework, ConVMLP-ResU-Net. Thereinto, the front-end ConVMLP integrates convolution (ConV) and multi-layer perceptrons (MLP) to capture localized and long-range spatial information, thus making ConVMLP excel in the location and coarse shape prediction of esophageal GTV. According to the PHRM, the front-end ConVMLP should have a strong generalization ability to ensure that the back-end ResU-Net has correct and valid reasoning. Therefore, a condition control training algorithm was proposed to control the training process of ConVMLP for a robust front end. Afterward, the back-end ResU-Net benefits from the yielded mask by ConVMLP to conduct a finer expansive segmentation to output the final result. Extensive experiments were carried out on a clinical cohort, which included 1138 pairs of 18F-FDG positron emission tomography/computed tomography (PET/CT) images. We report the Dice similarity coefficient, Hausdorff distance, and Mean surface distance as 0.82 ± 0.13, 4.31 ± 7.91 mm, and 1.42 ± 3.69 mm, respectively. The predicted contours visually have good agreements with the ground truths. The devised ConVMLP is apt at locating the esophageal GTV with correct initial shape prediction and hence facilitates the finer segmentation of the back-end ResU-Net. Both the qualitative and quantitative results validate the effectiveness of the proposed method.
The precision medicine era has seen increased utilization of artificial intelligence (AI) in the field of genetics. We sought to explore the ways that genetic counselors (GCs) currently use the publicly accessible AI tool Chat Generative Pre-trained Transformer (ChatGPT) in their work. GCs in North America were surveyed about how ChatGPT is used in different aspects of their work. Descriptive statistics were reported through frequencies and means. Of 118 GCs who completed the survey, 33.8% (40) reported using ChatGPT in their work; 47.5% (19) use it in clinical practice, 35% (14) use it in education, and 32.5% (13) use it in research. Most GCs (62.7%; 74) felt that it saves time on administrative tasks but the majority (82.2%; 97) felt that a paramount challenge was the risk of obtaining incorrect information. The majority of GCs not using ChatGPT (58.9%; 46) felt it was not necessary for their work. A considerable number of GCs in the field are using ChatGPT in different ways, but it is primarily helpful with tasks that involve writing. It has potential to streamline workflow issues encountered in clinical genetics, but practitioners need to be informed and uniformly trained about its limitations.
The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran. The performance of models was evaluated by using several quantitative and graphical criteria. The effect of parameter settings on the performance of deep models on SSL prediction was also investigated. The optimal optimization algorithms, maximum iteration (MI), and batch size (BC) were obtained for modeling daily SSL, and structure of the model impact on prediction remarkably. The comparison of prediction accuracy of the models illustrated that DDNN (with R2 = 0.96, RMSE = 333.46) outperformed LSTM (R2 = 0.75, RMSE = 786.20), GRU (R2 = 0.73, RMSE = 825.67), and simple RNN (R2 = 0.78, RMSE = 741.45). Furthermore, the Taylor diagram confirmed that DDNN has the highest performance among other models. Interpretation techniques can address the black-box nature of models, and here, SHAP was applied to develop an interpretable DL model to interpret of DL model's output. The results of SHAP showed that river discharge has the strongest impact on the model's output in estimating SSL. Overall, we conclude that DL models have great potential in watersheds to predict SSL. Therefore, different interpretation techniques as tools to interpret DL model's output (DL model is as black-box model) are recommended in future research.