Yoxas/statistical_literacy huggingface dataset:
Health systems that apply artificial intelligence (AI) are transforming the roles of healthcare providers, including those of Doctor of Nursing Practice (DNP) providers. These professionals are required to utilize informatics knowledge and skills to deliver quality care, necessitating a high level of informatics competencies, which should be developed through well-structured courses. The purpose of this study is to assess the informatics competency scale scores of DNP students and to provide recommendations for enhancing the informatics curriculum. An online informatics course was offered to students enrolled in a Bachelor of Science in Nursing to DNP program, and their informatics competency, which includes three subscales, was evaluated. Online survey data were collected from Fall 2021 to Fall 2022 using the "Self-Assessment of Informatics Competency Scale for Health Professionals." An analysis of 127 student responses revealed that students demonstrated competence in overall informatics competency and in one subscale: "applied computer skills (clinical informatics)." They showed proficiency in the "basic computer skills" and the "role" subscales. However, they reported lower competency in managing data and integrating standard terminology into their practice. The findings offer detailed insights into the current informatics competencies of DNP students and can inform informatics educators on how to enhance their courses. As healthcare institutions increasingly depend on AI applications, it is imperative for informatics educators to include AI-related content in their curricula.
Health-Related Quality of life (HRQoL) in cancer survivors can be significantly affected in the long-term by various consequences resulting from differing levels of severity of cancer and its treatments. Our objective was to identify factors associated with HRQoL in breast cancer survivors (BCSs) and cancer-free women (CFWs). We conducted a cross-sectional study in Seintinelles volunteers who answered online questionnaires between September 15, 2020 and February 5, 2021. HRQoL was measured using the World Health Organization Quality of Life-BREF questionnaire. We collected data on sociodemographic and health-related factors, lifestyle habits, coping mechanisms, locus of control, and health literacy. SAS version 9.4 statistical software was used for analyses. We performed descriptive analyses of the characteristics of the participants in each group and compared these characteristics between the two groups using the Chi2 test or the Student t-test. The adjusted means of the scores of different psychometric scales were calculated and compared using the method of least squares to fit general linear models (GLM) while adjusting for various factors. Multiple linear or multiple logistic regression models were used to assess the factors associated with WHOQOL-BREF scores, separately, in the two groups of participants. The study involved 722 BCSs and 1359 CFWs aged 26-75 years. BCSs had significantly lower physical health scores and were less likely to be satisfied with their health compared to CFWs (59.5 vs. 63.2, p < 0.0001; and 56.5% vs. 75.2%, p = 0.002, respectively). In both groups, some common factors were positively associated with physical health (high financial level, being professionally active, normal BMI, good health status, alcohol consumption, higher values (> 22) of internal locus of control); or inversely associated (neurological and sleep problems, over two medical consultations/year). In BCSs, treatment by mastectomy or radiation therapy/brachytherapy, a short-time since diagnosis, current cancer therapy, and presence of sequalae were inversely associated with physical health. BCSs' health satisfaction was diminished with lower values of coping by positive thinking (≤ 14) and seeking social support (≤ 18). HRQoL can be improved by developing strategies that increase internal locus of control and coping (positive thinking, problem-solving and seeking social support), and through health literacy.
Health-related quality of life is crucial for people dealing with chronic illness. This study investigated the quality of life in individuals with 5 common chronic conditions in Korea. We also analyzed socioeconomic factors such as education, income, occupation, and urbanization to identify determinants of inequality. Using 2016 Korea Community Health Survey data, we examined individuals aged 30 or older with chronic diseases (diabetes, hypertension, cardiovascular disease, hyperlipidemia, arthritis) using the EuroQol 5-Dimension 3 Level tool. We analyzed the associations between socioeconomic factors (education, income, occupation, urbanization) and quality of life using descriptive statistics and regression analysis. Inequality indices (relative inequality index, absolute inequality index) were used to measure inequality in quality of life. Individuals with higher income levels showed a 1.95-fold higher likelihood of a better quality of life than those with the lowest income. The lowest income group had higher odds of mobility (adjusted odds ratio [aOR], 2.2), self-care (aOR, 2.1), activity limitations (aOR, 2.4), pain/discomfort (aOR, 1.8), and anxiety/depression (aOR, 2.3). Educational disparities included a 3-fold increase in mobility and daily activity problems for those with elementary or lower education. Well-educated participants had a 1.94 times higher quality of life, with smaller differences in anxiety/depression and self-management. The income gap accounted for 14.1% of variance in quality-of-life disparities. Addressing socioeconomic disparities in the quality of life for individuals with chronic diseases necessitates tailored interventions and targeted health policies. This research informs policymakers in developing focused initiatives to alleviate health inequities. It emphasizes the importance of mental health support and ensuring affordable, accessible healthcare services.
Health-seeking behavior (HSB) is an individual's reaction to sickness, for which they seek medical attention, regardless of the form such care may take. Poor HSB is frequently associated with lower health outcomes and greater mortality and disease rates in a nation. Sociodemographic factors, including age, gender, family structure, occupation, ethnicity, and rates of literacy and poverty, can influence a person's HSB. This study would benefit Pakistani parents of the paediatric population to make more informed health choices for their children. This study aims to determine the influence of sociodemographic factors on parental HSB for illnesses among the pediatric age group in Karachi. A cross-sectional study was conducted in which 214 parents were interviewed about their choices for health-seeking healthcare for their pediatric children of the pediatric age group (birth-18 years), who were attending the pediatric OPD and wards at Dr. Ruth Pfau Civil Hospital, Karachi in May 2023. Only parents or guardians with sick children were included. Their sociodemographic characteristics and health-seeking choices were asked. Informed consent was obtained, and all data were recorded. SPSS version 25 was used for analysis. For the first choice, the majority of parents (82.7%) preferred to seek healthcare for their children from medical doctors, followed by spiritual healers (10.7%), traditional healers (5.6%), and homoeopathic doctors (0.9%). For the second choice, the highest percentage was for a doctor (76.2%), followed by a spiritual healer (18.7%), a homoeopathic doctor (3.3%), and a traditional healer (1.9%). A significant correlation was found between the first visit and the decision makers (p = 0.019), the father's education level of the father (p = 0.001), the mother's occupation of the mother (p = 0.019), and the mother's education level of the mother (p = 0.001). Sociodemographic characteristics greatly influence parents' HSBs of parents for their children. Despite having low-paying jobs and being uneducated, most people choose to refer to a doctor for their child due to awareness.
Healthcare avoidance and delay (HAD) in the transgender population has been well documented, and research has explored a range of associated factors that help to identify those most at risk of HAD. This review addresses a gap in the research by synthesizing research exploring associations between HAD and demographic factors. A systematic search of literature published at any time up to December 2021 was conducted, using five databases (EBSCO, EMBASE, PubMed, Scopus, and Web of Science) and manually searching reference lists of included studies. After exclusion of duplicates, 608 unique records were subjected to double screening. Papers reporting statistical analyses of HAD in association with any sociodemographic variables were included in this review. Papers consisted of nineteen cross-sectional studies. Narrative synthesis was used to address findings. Nineteen studies met inclusion criteria, exploring HAD in association with a wide range of demographic factors, including sex and gender, social transition factors, age, race and ethnicity, socioeconomic factors, veteran status, education, sexuality, relationship status, citizenship, place of residence, and state demographics. Findings identified intra-community demographic risk factors, with consistent evidence for increased HAD among transmasculine, and younger, participants. Lower income and higher educational attainment were also associated with increased HAD, while remaining areas had weak or little evidence for association with HAD. This review expands knowledge in this area by highlighting demographic factors associated with increased HAD in research literature, and exploring how these may be further investigated to address substantial gaps in the body of research.
Healthcare data analytics is a methodological approach to the systematic analysis of health data, and it provides opportunities for healthcare professionals to improve health system management, patient engagement, budgeting, planning and performing evidence-based decision-making. Literature suggests that certain skills and/or competencies for health professionals working with big data in health care would be required. A review of the skills and competencies in health data analytics required by health professionals is needed to support the development or re-engineering of curriculum for health professionals to ensure they develop the abilities to make evidence-based decisions that ultimately can lead to the effective and efficient functioning of a healthcare system. Using Arksey and O'Malley's framework, this study will review literature published in English from January 2012 to December 2022. The database search includes Academic Search Complete, CINAHL, and MEDLINE via EBSCOhost, PubMed, Science Direct, Scopus, and Taylor and Francis. The reference lists of key studies will be searched to identify additional appropriate studies to include. The review will be conducted using an inclusion and exclusion criteria. Iterative processes will be involved at the various stages of search strategy piloting, screening and data extraction. Articles will be reviewed through a two-step process (title and abstract, and full-text review) by at least two reviewers. Data will be described quantitatively and/or qualitatively and presented in diagrams and tables. Ethical clearance has been received, and strict protocol measures will be followed to ensure the data reported is of quality and relevant to the review purpose. The results will be disseminated through a peer-reviewed scientific journal, presentation at national and/or international conferences, and other platforms such as social media (eg, LinkedIn, Twitter), and relevant stakeholders.
Healthcare facility characteristics, such as ownership, size, and location, have been associated with patient outcomes. However, it is not known whether the outcomes of healthcare workers are associated with the characteristics of their employing healthcare facilities, particularly during the COVID-19 pandemic. This was an analysis of a nationwide registry of healthcare workers (the Healthcare Worker Exposure Response and Outcomes (HERO) registry). Participants were surveyed on their personal, employment, and medical characteristics, as well as our primary study outcomes of COVID-19 infection, access to personal protective equipment, and burnout. Participants from healthcare sites with at least ten respondents were included, and these sites were linked to American Hospital Association data to extract information about sites, including number of beds, teaching status, urban/rural location, and for-profit status. Generalized estimating equations were used to estimate linear regression models for the unadjusted and adjusted associations between healthcare facility characteristics and outcomes. A total of 8,941 healthcare workers from 97 clinical sites were included in the study. After adjustment for participant demographics, healthcare role, and medical comorbidities, facility for-profit status was associated with greater odds of COVID-19 diagnosis (aOR 1.76, 95% CI 1.02-3.03, p = .042). Micropolitan location was associated with decreased odds of COVID-19 infection after adjustment (aOR = 0.42, 95% CI 0.24, 0.71, p = .002. For-profit facility status was associated with decreased odds of burnout after adjustment (aOR = 0.53, 95% CI 0.29-0.98), p = .044). For-profit status of employing healthcare facilities was associated with greater odds of COVID-19 diagnosis but decreased odds of burnout after adjustment for demographics, healthcare role, and medical comorbidities. Future research to understand the relationship between facility ownership status and healthcare outcomes is needed to promote wellbeing in the healthcare workforce. The registry was prospectively registered: ClinicalTrials.gov Identifier (trial registration number) NCT04342806, submitted April 8, 2020.
Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks.
Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility. We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts. EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science. As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied. This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.
Healthcare practitioners delivering services in rural and underserved areas need timely access to appropriate knowledge to optimise the care they deliver. Novice generalist occupational therapists in South Africa experience this need as they respond to a high demand for hand therapy. Embedded within a study aimed at identifying their support and development needs, this article describes participants' experience of a virtual community of practice. A qualitative case study design was employed. Nine occupational therapists participated in a virtual community of practice that met fortnightly for meetings and interacted on WhatsApp. Data were collected through photo elicitation, facilitated reflection, and case discussions. An online survey questionnaire was used to evaluate participants' experience of this virtual community. Thematic analysis was applied to the anonymous responses submitted by participants (n=7). A number of strategies were employed to ensure the trustworthiness of results including prolonged engagement, member checking, peer examination, reflexive reading and writing, triangulation, and a dense description of participants to enable readers to evaluate the transferability of results. Three themes were generated from analysis. The first theme, versatile support, describes participants' experience of being helped and supported, appreciating the immediacy of support, and being able to share resources. A vehicle for learning captures participants' experience of mutual learning, opportunity to reflect, to acquire knowledge and skills, and develop their clinical reasoning. Finally, the community of practice was grounding: learning opportunities were contextually relevant and participants were able to consolidate their professional values and identity. Participants raised the importance of using online platforms that were accessible, recommended a group size of 5-10 members, and proposed 60-90-minute meetings held weekly or fortnightly. A virtual community of practice provided both support and professional development opportunities for therapists delivering hand therapy. Careful planning and implementation to upscale this intervention are recommended for rehabilitation personnel delivering care to underserved communities in South Africa. The logistics of virtual communities need to mitigate for connectivity difficulties, and online platforms should enable real-time support. Participant satisfaction and the evaluation of implementation outcomes should be considered in the design of virtual communities of practice.
Healthcare professionals (HCPs) face a variety of work-related stressors that have impact on their mental health and cognitive performance. Work resilience is a psychological resource that helps workers cope with stress and prevents unfavorable psychological impact. The aim of this study was to assess the associations between working as HCPs and cognitive function as well as work resilience. This was a comparative cross-sectional study conducted among HCPs at Suez Canal University Hospital in Ismailia Governorate, Egypt, during April 2023 to August 2023. Two hundred and thirty-five HCPs and 107 administrative employees (Admins) were invited to participate in this study. A self-administered questionnaire was used to obtain sociodemographic and other relavent data. Cognitive function was assessed with the Mini-Mental State Examination test; work resilience was assessed with the Brief Resilience Scale; and psychological distress was measured with the Depression, Anxiety, and Stress Scale - 21-items (DASS-21) scale. Statistical significance was determined by Mann Whitney U-test for continuous variables, and Chi-square test or Fisher's exact, as appropriate, for categorical variables. Multiple logistic regression models were employed to determine associations between the main outcomes (cognitive impairment and low resilience) and the main covariate (working as HCPs vs. Admins), adjusting for all potential confounders. HCPs showed a significantly greater cognitive impairment, less resilience, and DASS-21 than the Admins. The odds of impaired cognitive function in HCPs were significantly higher than the Admins (odds ratio [OR]: 4.45, 95% confidence interval [CI]: 1.27-15.67, P = 0.020), adjusted for all potential covariates. Similarly, the odds of low resilience in HCPs were significantly higher than Admins (OR: 5.81, 95% CI: 2.72-12.44, P < 0.001), adjusted for all potential covariates. However, the adjusted association between impaired cognitive function and low resilience was not statistically significant (OR: 0.55, 95% CI: 0.23-1.33, P = 0.185). HCPs had significantly impaired cognitive function and low work resilience. Workplace policies and interventions to control depression, stress, and anxiety are required as it is the encouragement of physical activity. Programs that combine positive coping skills training (e.g., relaxation training, positive thinking, and problem solving) with resilience-building interventions (e.g., taking a proactive approach to solving problems, being flexible and adaptive) should be developed, with special attention to HCPs who have a higher sense of self-efficacy.