Yoxas/statistical_literacy huggingface dataset:
Adult health outcomes are linked to childhood factors such as socioeconomic status via cultural health capital (CHC). Specifically, these factors shape opportunities for developing skills for navigating health environments via experience and the intergenerational transfer of health-related knowledge and skills. Health literacy (HL) is considered a part of and/or result of CHC. HL develops similarly to CHC via opportunities and experiences. Most research to date has ignored the effect of childhood factors on adult HL. The purpose of this study was to explore how childhood factors are related to adult HL. Data were collected from adults (N = 736, mean age = 40.65 years, standard deviation [SD] = 15.39; 52% female; 53.8% White, 31.3% Hispanic and Latino/a/e) in the United States using Qualtrics Panel. Multivariate ordinal and binary logistic regressions predicting HL (as measured by the Newest Vital Sign and Single-Item Literacy Scale) from childhood factors and accounting for demographic covariates were estimated. After accounting for covariates, such as the presence of an employed adult in a white-collar (odds ratio [OR] = 3.34) or blue-collar (OR = 3.68) occupation (versus unknown/not employed) increased the odds of being categorized as possible limited literacy and adequate literacy (versus limited literacy) as measured by the Newest Vital Sign. Similarly, having an employed adult during childhood who had a blue-collar occupation (vs. unknown/not employed) increased the odds of being categorized as adequate literacy (OR = 2.06) as measured by the Single-Item Literacy Scale. Because the adult's employment played a role in the child's adult HL after accounting for other factors and demographics, these findings support using a lifespan approach to assess and identify risk factors for lower HL. This study contributes to the growing body of evidence of how HL is interconnected with social determinants of health across the lifespan and the need to address HL skills in those with poor social determinants of health. [HLRP: Health Literacy Research and Practice. 2024;8(2):e79-e88. ].
Adults hospitalized for cardiovascular events are at high risk for postdischarge mortality. Screening of psychosocial risk is prioritized by the Joint Commission. We tested whether key patient-reported psychosocial and behavioral measures could predict posthospitalization mortality in a cohort of adults hospitalized for a cardiovascular event. We conducted a prospective cohort study to test the prognostic utility of validated patient-reported measures, including health literacy, social support, health behaviors and disease management, and socioeconomic status. Cox survival analyses of mortality were conducted over a median of 3.5 years. Among 2977 adults hospitalized for either acute coronary syndrome or acute decompensated heart failure, the mean age was 53 years, and 60% were male. After adjusting for demographic, clinical, and other psychosocial factors, mortality risk was greatest among patients who reported being unemployed (hazard ratio [HR]: 1.99, 95% confidence interval [CI]): 1.30-3.06), retired (HR: 2.14, 95% CI: 1.60-2.87), or unable to work due to disability (HR: 2.36, 95% CI: 1.73-3.21), as compared to those who were employed. Patient-reported perceived health competence (PHCS-2) and exercise frequency were also associated with mortality risk after adjusting for all other variables (HR: 0.86, 95% CI: 0.73-1.00 per four-point increase in PHCS-2; HR: 0.86, 95% CI: 0.77-0.96 per 3-day increase in exercise frequency, respectively). Patient-reported measures of employment status, perceived health competence, and exercise frequency independently predict mortality after a cardiac hospitalization. Incorporating these brief, valid measures into hospital-based screening may help with prognostication and targeting patients for resources during post-discharge transitions of care.
Adults with rare autoimmune rheumatic diseases face unique challenges and struggles to navigate health-care systems designed to manage common conditions. Evidence to inform an optimal service framework for their care is scarce. Using systemic vasculitis as an exemplar, we aimed to identify and explain the key service components underpinning effective care for rare diseases. In this mixed-methods study, data were collected as part of a survey of vasculitis service providers across the UK and Ireland, interviews with patients, and from organisational case studies to identify key service components that enable good care. The association between these components and patient outcomes (eg, serious infections, mortality) and provider outcomes (eg, emergency hospital admissions) were examined in a population-based data linkage study using routine health-care data obtained from patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis from national health datasets in Scotland. We did univariable and multivariable analyses using Bayesian poisson and negative binomial regression to estimate incident rate ratios (IRRs), and Cox proportional hazards models to estimate hazard ratios (HRs). People with lived experiences were involved in the research and writing process. Good care was characterised by service components that supported timely access to services, integrated care, and expertise. In 1420 patients with ANCA-associated vasculitis identified from national health datasets, service-reported average waiting times for new patients of less than 1 week were associated with fewer serious infections (IRR 0·70 [95% credibility interval 0·55-0·88]) and fewer emergency hospital admissions (0·78 [0·68-0·92]). Nurse-led advice lines were associated with fewer serious infections (0·76 [0·58-0·93]) and fewer emergency hospital admissions (0·85 [0·74-0·96]). Average waiting times for new patients of less than 1 week were also associated with reduced mortality (HR 0·59 [95% credibility interval 0·37-0·93]). Cohorted clinics, nurse-led clinics, and specialist vasculitis multi-disciplinary team meetings were associated with fewer serious infections (IRR 0·75 [0·59-0·96] for cohorted clinics; 0·65 [0·39-0·84] for nurse-led clinics; 0·72 [0·57-0·90] for specialist vasculitis multi-disciplinary team meetings) and emergency hospital admissions (0·81 [0·71-0·91]; 0·75 [0·65-0·94]; 0·86 [0·75-0·96]). Key components were characterised by their ability to overcome professional tensions between specialties. Key service components associated with important health outcomes and underpinning factors were identified to inform initiatives to improve the design, delivery, and effectiveness of health-care models for rare autoimmune rheumatic diseases. Versus Arthritis.
Advance care planning enables people to record their future health and care wishes and appoint someone as an advocate. An advance directive can be made in the event that a person is incapacitated, so that their wishes are still upheld. The beliefs of the nephrology team might affect patients' choices and willingness to sign an advance directive. To increase the number of dialysis patients who have signed an advance directive, it is necessary to educate the nephrology medical staff. To explore the intention to sign an advance directive and its related factors among nephrology medical staff. A cross-sectional and correlational design was used. This study recruited 160 nephrology medical staff. Data were analysed by using the Statistical Package for Social Science 21.0 for Windows. The results found that the longer someone has worked as part of the nephology medical staff, the more knowledgeable they were about an advance directive. This led to them being more likely to want to sign an advance directive for themselves. In order to improve the knowledge and awareness of advance directives among nephrology medical staff, hospital managers should provide continuing education on this topic.
Advance care planning is vital for ensuring individuals receive end-of-life care that is consistent with their care preferences and improves patient quality of life and satisfaction with care; however, only 11% of Americans have discussed advance care planning with a healthcare provider. Individuals with limited health literacy are even less likely to participate in advance care planning due to difficulty comprehending complex health information. The purpose of this review was to identify randomized controlled trials designed to address the effects of limited health literacy on advance care planning, evaluate the quality of these studies, and summarize evaluation data to inform future studies. This systematic review examined randomized controlled trials published from January 1997 to July 2020 using the PubMed, CINAHL, PsycINFO, and Scopus databases. Data were extracted and two reviewers independently evaluated the quality of studies using the Joanna Briggs Institute Critical Appraisal Tool. The database search yielded 253 studies and five studies were included in the final review. Studies were conducted in mostly White patients in outpatient clinics in the United States. Researchers wrote text at lower reading levels, added images to materials, and created videos to enhance communication. Health literacy interventions increased participant knowledge, preference for comfort care, engagement, and care documentation; however, several methodological issues were identified, including baseline differences in treatment and control groups, issues with blinding, lack of valid and reliable outcome measures, and inappropriate statistical analyses. More high-quality intervention studies that address the effects of limited health literacy on advance care planning in diverse populations and settings are needed. Future intervention studies should use reliable and valid instruments to measure advance care planning outcomes. Clinicians should use materials appropriate for their patients' health literacy levels to address their advance care planning needs.
Advanced diagnostic bronchoscopy includes endobronchial ultrasound (EBUS) guided transbronchial lung and lymph node biopsies, CT navigation and robotic bronchoscopy. Interventional bronchoscopy refers to procedures performed for therapeutic purposes such as balloon dilation of the airway, tissue debulking, cryotherapy, removal of foreign bodies and insertion of endobronchial valves [1]. For adult patients, these procedures are standard of care [2, 3]. Despite a lack of formalized training, there are numerous case reports and case series describing the use of advanced diagnostic and interventional bronchoscopy techniques in children. The safety and feasibility of EBUS-TBNA, cryotherapy techniques, endobronchial valves among other techniques have been demonstrated in these publications [1, 4-9]. We sought to better understand the current practices and perspectives on interventional and advanced bronchoscopy among pediatric pulmonologists through surveys sent to pediatric teaching hospitals across the United States. We received 43 responses representing 28 programs from 25 states. The highest bronchoscopy procedure volume occurred in the 0-5 years age group. Among our respondents, 31% self-identified as a pediatric interventional/advanced bronchoscopist. 79% believe that advanced and interventional training is feasible in pediatric pulmonology and 77% believe it should be offered to pediatric pulmonary fellows. This is the first study to characterize current practices and perspectives regarding advanced diagnostic and interventional bronchoscopy procedures among pediatric pulmonologists in the United States. Pediatric interventional pulmonology (IP) is in its infancy and its beginnings echo those of the adult IP where only certain centers were performing these procedures.
Advanced practice registered nurses (APRNs) and physician assistants (PAs) are integral to health care delivery in the United States. However, the cultivation of APRNs and PAs relies heavily on the model of precepting. Advanced practice registered nurses and PAs frequently precept students or new hires, yet limited data are available to describe the motivations, incentives, and barriers associated with precepting. The purpose of this mixed-method, cross-sectional study was to better understand APRN and PA preceptors' perceived levels of support during precepting, facilitating factors, and barriers to the precepting process. An electronic survey was distributed via REDCap to a convenience sample of APRN and PA preceptors at an academic medical center. Data collected from the survey were analyzed using both descriptive statistics and qualitative thematic analysis. One hundred fifty-four participants completed the survey. Motivating factors for precepting included "giving back" to the profession, and barriers included inadequate time to precept while in the clinical role. Participants indicated that financial incentives, heightened communication, protected teaching time, preceptor training, meaningful recognition, and organizational support could enhance the precepting process. This study demonstrated that altruistic intentions frequently motivate APRN and PA preceptors, however, navigating multiple clinical responsibilities while precepting serves as a barrier to the precepting process. Preceptor training, ongoing education, dedicated time for precepting, enhanced communication, and organizational or professional incentives may optimize the APRN and PA precepting process. Further research should focus on how to optimally incorporate education, professional development, support, and incentives into the APRN and PA preceptor role.
Advanced therapies offer unprecedented opportunities for treating rare neurological disorders (RNDs) in children. However, health literacy, perceptions and understanding of novel therapies need elucidation across the RND community. This study explored healthcare professionals' and carers' perspectives of advanced therapies in childhood-onset RNDs. In this mixed-methodology cross-sectional study, 20 healthcare professionals (clinicians, genetic counsellors and scientists) and 20 carers completed qualitative semistructured interviews and custom-designed surveys. Carers undertook validated psychosocial questionnaires. Thematic and quantitative data analysis followed. Participants described high positive interest in advanced therapies, but low knowledge of, and access to, reliable information. The substantial 'therapeutic gap' and 'therapeutic odyssey' common to RNDs were recognised in five key themes: (i) unmet need and urgency for access; (ii) seeking information; (iii) access, equity and sustainability; (iv) a multidisciplinary and integrated approach to care and support and (v) difficult decision-making. Participants were motivated to intensify RND clinical trial activity and access to advanced therapies; however, concerns around informed consent, first-in-human trials and clinical trial procedures were evident. There was high-risk tolerance despite substantial uncertainties and knowledge gaps. RNDs with high mortality, increased functional burdens and no alternative therapies were consistently prioritised for the development of advanced therapies. However, little consensus existed on prioritisation to treatment access. This study highlights the need to increase clinician and health system readiness for the clinical translation of advanced therapeutics for RNDs. Co-development and use of educational and psychosocial resources to support clinical decision-making, set therapeutic expectations and promotion of equitable, effective and safe delivery of advanced therapies are essential. Participant insights into the psychosocial burden and information need to enhance the delivery of care in this formative study are informing ongoing partnerships with families, including co-production and dissemination of psychoeducational resources featuring their voices hosted on the Sydney Children's Hospitals Network website SCHN Brain-Aid Resources.
Advancement of research in education has propelled the augmentation of theoretical and practical knowledge in learning-oriented feedback. In recent years, the channels, modes, and orientations of feedback became manifold. Copious empirical evidence from the body of literature supported the strength of feedback in enhancing learning outcomes and promoting the motivation of learners. However, compared to the popularity in implementation and fruitfulness of findings in other educational domains, the application of state-of-the-art technology-enhanced feedback in fostering students' L2 oral abilities remain few and far between. To address the knowledge gap, the present study endeavored to investigate the effect of Danmaku-based and synchronous peer feedback on L2 oral performance and the acceptance thereof among students. Adopting a mixed-method design, the study recruited 74 (n = 74) undergraduate English majors from a Chinese university for a 16-week 2x2 experiment. The collected data were analyzed through statistical and thematic analysis respectively. The findings revealed that Danmaku-based and synchronous peer feed-back was impactful on students' performance in L2 oral production. Furthermore, the impacts of peer feedback on subdomains of L2 competence were statistically analyzed. Regarding students' perceptions, the incorporation of peer feedback was generally favored by participants who were satisfied and motivated in the learning process but lacked confidence in their assessment literacy. Furthermore, students expressed their agreement with the benefit of reflective learning and the subsequent enrichment in knowledge and horizon. The research was significant for its conceptual and practical contribution for follow-up researchers and educators in L2 education and learning-oriented feedback.
Advances in artificial intelligence (AI) have given rise to sophisticated algorithms capable of generating human-like text. The goal of this study was to evaluate the ability of human reviewers to reliably differentiate personal statements (PS) written by human authors from those generated by AI software. Four personal statements from the archives of two surgical program directors were de-identified and used as the human samples. Two AI platforms were used to generate nine additional PS. Four surgeons from the residency selection committees of two surgical residency programs of a large multihospital system served as blinded reviewers. AI was also asked to evaluate each PS sample for authorship. Sensitivity, specificity and accuracy of the reviewers in identifying the PS author were calculated. Kappa statistic for correlation between the hypothesized author and the true author were calculated. Inter-rater reliability was calculated using the kappa statistic with Light's modification given more than two reviewers in a fully-crossed design. Logistic regression was performed with to model the impact of perceived creativity, writing quality, and authorship or the likelihood of offering an interview. Human reviewer sensitivity for identifying an AI-generated PS was 0.87 with specificity of 0.37 and overall accuracy of 0.55. The level of agreement by kappa statistic of the reviewer estimate of authorship and the true authorship was 0.19 (slight agreement). The reviewers themselves had an inter-rater reliability of 0.067 (poor), with only complete agreement (four out of four reviewers) on two PS, both authored by humans. The odds ratio of offering an interview (compared to a composite of "backup" status or no interview) to a perceived human author was 7 times that of a perceived AI author (95% confidence interval 1.5276 to 32.0758, p=0.0144). AI hypothesized human authorship for twelve of the PS, with the last one "unsure." The increasing pervasiveness of AI will have far-reaching effects including on the resident application and recruitment process. Identifying AI-generated personal statements is exceedingly difficult. With the decreasing availability of objective data to assess applicants, a review and potential restructuring of the approach to resident recruitment may be warranted.
Advances in computer technology, patient monitoring systems, and electronic health record systems have enabled rapid accumulation of patient data in electronic form (i.e. big data). Organizations such as the Anesthesia Quality Institute and Multicenter Perioperative Outcomes Group have spearheaded large-scale efforts to collect anaesthesia big data for outcomes research and quality improvement. Analytics--the systematic use of data combined with quantitative and qualitative analysis to make decisions--can be applied to big data for quality and performance improvements, such as predictive risk assessment, clinical decision support, and resource management. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces, and it can facilitate performance of cognitive activities involving big data. Ongoing integration of big data and analytics within anaesthesia and health care will increase demand for anaesthesia professionals who are well versed in both the medical and the information sciences.
Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system's ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles' operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model's performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms.