Yoxas/statistical_literacy huggingface dataset:
Disparities in the timely diagnosis and care of cancer patients, particularly concerning geographical, racial/ethnic, and economic factors, remain a global health challenge. This study explores the multifaceted interplay between socioeconomic status, health literacy, and specific patient perceptions regarding care access and treatment options that impact cancer care in Uruguay. Using the Cancer Health Literacy Test, Spanish Version (CHLT-30-DKspa), and a highly comprehensive questionnaire, we dissected the factors influencing the pathway to diagnosis and route of cancer care. This was done to identify delays by analyzing diverse socioeconomic and sex subgroups across multiple healthcare settings. Patients with lower income took longer to get an appointment after showing symptoms (p = 0.02) and longer to get a diagnosis after having an appointment (p = 0.037). Race/ethnicity also had a significant impact on the length of time from symptoms to first appointment (p =0.019), whereas employment status had a significant impact on patients being susceptible to diagnostic delays beyond the advocated 14-day window (p = 0.02). Higher educational levels were positively associated with increased cancer health literacy scores (p = 0.043), revealing the potential to mitigate delays through health literacy-boosting initiatives. Women had significantly higher self-reported symptom duration before seeking an intervention (p = 0.022). We also found many other significant factors effecting treatment delays and cancer health literacy. While affirming the global pertinence of socioeconomic- and literacy-focused interventions in enhancing cancer care, the findings underscore a complex, gendered, and perceptually influenced healthcare navigation journey. The results highlight the urgent necessity for strategically crafted, globally relevant interventions that transcend equitable access to integrate literacy, gender sensitivity, and patient-perception alignments in pursuit of optimized global cancer care outcomes.
Disposition decision-making in the emergency department (ED) is crucial to patient safety and quality of care. It can inform better care, lower chance of infections, appropriate follow-up care, and reduced healthcare costs. The aim of this study was to examine correlates of ED disposition among adult patients at a teaching and referral hospital based on patients' demographic, socioeconomic, and clinical characteristics. A cross-sectional study conducted at the ED of the King Abdulaziz Medical City hospital in Riyadh. A two-level validated questionnaire was used-a patient questionnaire and healthcare staff/facility survey. The survey employed a systematic random sampling technique to recruit subjects at a pre-specified interval as patients arrived at the registration desk. We analyzed 303 adult patients visiting the ED, who were triaged, consented to participate in the study, completed the survey, and admitted to a hospital bed or discharged home. We used descriptive and inferential statistics to summarize and determine the interdependence and relationships of variables. We used logistic multivariate regression analysis to establish relationships and the odds of admission to a hospital bed. The mean age of the patients was 50.9 (SD = 21.4, Range 18 to 101). A total of 201 (66%) were discharged home while the rest were admitted to a hospital bed. Results of the unadjusted analysis suggest that older patients, males, patients with low level of education, and those with comorbidities and middle-income were more likely to be admitted to the hospital. The results of the multivariate analysis suggest that patients with comorbidities, urgent conditions, prior history of hospitalization, and higher triage levels were more likely to be admitted to a hospital bed. Having proper triage and timely stopgap review measures in the admission process can help new patients to locations that best support their needs and improve the quality and efficiency of the facility. The findings may be a sentinel indicator that informs overuse or inappropriate use of EDs for non-emergency care, which is a concern in the Saudi Arabian publicly funded health system.
Disproportionality analyses using reports of suspected adverse drug reactions are the most commonly used quantitative methods for detecting safety signals in pharmacovigilance. However, their methods and results are generally poorly reported in published articles and existing guidelines do not capture the specific features of disproportionality analyses. We here describe the development of a guideline (REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance [READUS-PV]) for reporting the results of disproportionality analyses in articles and abstracts. We established a group of 34 international experts from universities, the pharmaceutical industry, and regulatory agencies, with expertise in pharmacovigilance, disproportionality analyses, and assessment of safety signals. We followed a three-step process to develop the checklist: (1) an open-text survey to generate a first list of items; (2) an online Delphi method to select and rephrase the most important items; (3) a final online consensus meeting. Among the panel members, 33 experts responded to round 1 and 30 to round 2 of the Delphi and 25 participated to the consensus meeting. Overall, 60 recommendations for the main body of the manuscript and 13 recommendations for the abstracts were retained by participants after the Delphi method. After merging of some items together and the online consensus meeting, the READUS-PV guidelines comprise a checklist of 32 recommendations, in 14 items, for the reporting of disproportionality analyses in the main body text and four items, comprising 12 recommendations, for abstracts. The READUS-PV guidelines will support authors, editors, peer-reviewers, and users of disproportionality analyses using individual case safety report databases. Adopting these guidelines will lead to more transparent, comprehensive, and accurate reporting and interpretation of disproportionality analyses, facilitating the integration with other sources of evidence.
Disruptive behavior of patients in acute psychiatric care is a problem for both patients and staff. Preventing a patient's impending disruption requires recognizing and understanding early signals. There are indications that a change in a patient's global functioning may be such a signal. The global functioning of patients is a multidimensional view on their functioning. It captures a patient's psychological symptoms, social skills, symptoms of violence, and activities in daily living. The aim of this study was to gain insight into the predictive value of global functioning on the risk of disruptive behavior of patients in acute psychiatric care. Also assessed was the time elapsed between the change in global functioning and a patient's disruptive behavior, which is necessary to know for purposes of early intervention. In a longitudinal retrospective study, we used daily measurements with the Brøset Violence Checklist (BVC) and the Kennedy Axis V (K-As) of each patient admitted to two acute psychiatric units over a period of six years. Data from 931 patients for the first 28 days after their admission were used for survival analysis and cox regression analysis. Disruptive behavior was mostly observed during the first days of hospitalization. Global functioning predicted disruptive behavior from the very first day of hospitalization. A cut-off score of 48 or lower on the K-As on the first admission day predicted a higher risk of disruptive behavior. If functioning remained poor or deteriorated substantially over three days, this was an additional signal of increased risk of disruptive behavior. Improvement in global functioning was associated with a decreased risk of disruptive behavior. More attention is needed for early interventions on global functioning to prevent disruptive behavior.
Dissociation is a conscious state characterized by alterations in sensation and perception and is thought to arise from traumatic life experiences. Previous research has demonstrated that individuals with high levels of dissociation show impairments in cognitive-emotional processes. Therefore, using the Competing Neurobehavioral Decisions System (CNDS) theory, we used statistical modeling to examine whether dissociative experience and trauma symptoms are independently predicted by impulsivity, risk-seeking, affective state (i.e., anxiety, depression, stress, and negative affect), and trauma history. In this cross-sectional study design, data were collected via Amazon Mechanical Turk from a total of n = 557 English-speaking participants in the United States. Using Qualtrics, participants answered a series of self-reported questionnaires and completed several neurocognitive tasks. Three independent multiple linear regression models were conducted to assess whether impulsivity, risk seeking, affective state, and trauma history predict depersonalization, trauma symptoms, and PTSD symptoms. As hypothesized, we found that depersonalization and other trauma symptoms are associated with heightened impulsivity, increased risk-seeking, impaired affective states, and a history of traumatic experiences. We demonstrate that an imbalanced CNDS (i.e., hyperimpulsive/hypoexecutive), as evidenced by decreased future valuation, increased risk seeking, and impaired affective states, predicts heightened depersonalization and other trauma and PTSD symptomatology. This is the first time that dissociation has been connected to delay discounting (i.e., the tendency to place more value on rewards received immediately compared to farther in the future). Interventions that positively impact areas of the CNDS, such as episodic future thinking or mindfulness meditation, may be a target to help decrease dissociative symptoms.
Distributed healthcare settings such as district hospitals, primary care, and public health facilities are becoming the real-life settings for workplace-based learning required to educate the future healthcare workforce. Therefore, a major focus should be on designing and developing workplace-based learning in these learning environments. Healthcare professionals and educational policymakers play a significant role in these settings as role models in workplace-based learning, and as leaders in integrating learning into their work environments. It is relevant to explore their beliefs, attitudes, and behaviors towards workplace-based learning in their own settings, in order to provide context-relevant recommendations that can assist in shaping workplace-based learning environments. We used individual interviews to understand professionals' experiences with workplace-based learning in distributed healthcare settings. We - three clinicians, an educationalist, and a philosopher - thematically analyzed transcripts of 13 interviews with healthcare professionals and educational policymakers from different healthcare settings who were involved in the clinical phase of undergraduate medical education. Clustering and categorizing of the data led to the construction of five overarching themes: Identification with and attitude towards medical education, Sense of ownership, Perceived time and space, Mutual preconceptions and relations, and Curriculum for a changing profession. These themes accentuate aspects relevant to the development of workplace-based learning in distributed healthcare settings on the individual, team, or organizational level. We highlight the significance of individual professionals in the development of workplace-based learning and emphasize the need for recognition and support for those occupying the 'broker' role at the intersection of education and practice. For future research and educational practice, we recommend prioritizing initiatives that build on good-practices in workplace-based learning and involve dedicated individuals in distributed healthcare settings.
Disturbances of the narrative self and personal identity accompany the onset of psychotic disorders in late adolescence and early adulthood (a formative developmental stage for self-concept and personal narratives). However, these issues have primarily been studied retrospectively after illness onset, limiting any inferences about their developmental course. Youth at clinical high risk for psychosis (CHR) (n = 49) and matched healthy comparison youth (n = 52) completed a life story interview (including self-defining memory, turning point, life challenge, and psychotic-like experience) and questionnaires assessing self-esteem, self-beliefs, self-concept clarity, and ruminative/reflective self-focus. Trained raters coded interviews for narrative identity themes of emotional tone, agency, temporal coherence, context coherence, self-event connections, and meaning-making (intraclass correlations >0.75). Statistical analyses tested group differences and relationships between self-concept, narrative identity, symptoms, and functioning. CHR participants reported more negative self-esteem and self-beliefs, poorer self-concept clarity, and more ruminative self-focus, all of which related to negative symptoms. CHR participants narrated their life stories with themes of negative emotion and passivity (ie, lack of personal agency), which related to positive and negative symptoms. Reflective self-focus and autobiographical reasoning were unaffected and correlated. Autobiographical reasoning was uniquely associated with preserved role functioning. This group of youth at CHR exhibited some, but not all, changes to self-concept and narrative identity seen in psychotic disorders. A core theme of negativity, uncertainty, and passivity ran through their semantic and narrative self-representations. Preserved self-reflection and autobiographical reasoning suggest sources of resilience and potential footholds for cognitive-behavioral and metacognitive interventions.
Disturbed sleep in patients with COPD impact quality of life and predict adverse outcomes. To identify distinct phenotypic clusters of patients with COPD using objective sleep parameters and evaluate the associations between clusters and all-cause mortality to inform risk stratification. A longitudinal observational cohort study using nationwide Veterans Health Administration data of patients with COPD investigated for sleep disorders. Sleep parameters were extracted from polysomnography physician interpretation using a validated natural language processing algorithm. We performed cluster analysis using an unsupervised machine learning algorithm (K-means) and examined the association between clusters and mortality using Cox regression analysis, adjusted for potential confounders, and visualized with Kaplan-Meier estimates. Among 9992 patients with COPD and a clinically indicated baseline polysomnogram, we identified five distinct clusters based on age, comorbidity burden and sleep parameters. Overall mortality increased from 9.4 % to 42 % and short-term mortality (<5.3 years) ranged from 3.4 % to 24.3 % in Cluster 1 to 5. In Cluster 1 younger age, in 5 high comorbidity burden and in the other three clusters, total sleep time and sleep efficiency had significant associations with mortality. We identified five distinct clinical clusters and highlighted the significant association between total sleep time and sleep efficiency on mortality. The identified clusters highlight the importance of objective sleep parameters in determining mortality risk and phenotypic characterization in this population.
Diverging death risks are associated with a wide range of social factors, including not only education and income but also other economic and non-economic resources. The aim of this study was to assess the association of mortality risks with four types of resources: economic, social, cultural and person capital. We used data of 2,952 participants from the Disparities in the Netherlands survey and annual mortality data from Statistics Netherlands for the period 2014 to 2021. Economic capital was measured through education, income, occupation, home equity, and liquid assets. Social capital was measured by the strength of social ties, the size of the core discussion network, and access to people in resourceful positions; cultural capital by lifestyle, digital skills, and mastery of English, and person capital by self-rated health, impediments to climbing stairs, self-confidence, self-image, people's appearance, and body mass index. To accommodate the fact that each capital was derived from several indicators, we used Partial Least Squares (PLS) Cox Regression. In multiple regression, higher economic, cultural, and person capital were associated with lower mortality (hazard ratio, 0.77; 95% confidence interval [CI, 0.65 to 0.90], 0.77 [0.64-0.93] and 0.80; [0.70-0.92]), adjusted for all capital measures and sex. The finding that more economic, cultural and person capital is associated with lower mortality provides empirical support for an approach that uses a broad spectrum of capital measures - hitherto rarely included simultaneously in epidemiological research - in order to understand diverging death risks. By integrating sociological concepts, cohort data, and epidemiological research methods, our study highlights the need for further research on the interplay between different forms of resources in shaping health inequalities. In designing public health interventions, we advocate the adoption of a multidimensional capital-based framework for tackling social disparities in mortality.
Diverse T and B cell repertoires play an important role in mounting effective immune responses against a wide range of pathogens and malignant cells. The number of unique T and B cell clones is characterized by T and B cell receptors (TCRs and BCRs), respectively. Although receptor sequences are generated probabilistically by recombination processes, clinical studies found a high degree of sharing of TCRs and BCRs among different individuals. In this work, we use a general probabilistic model for T/B cell receptor clone abundances to define "publicness" or "privateness" and information-theoretic measures for comparing the frequency of sampled sequences observed across different individuals. We derive mathematical formulae to quantify the mean and the variances of clone richness and overlap. Our results can be used to evaluate the effect of different sampling protocols on abundances of clones within an individual as well as the commonality of clones across individuals. Using synthetic and empirical TCR amino acid sequence data, we perform simulations to study expected clonal commonalities across multiple individuals. Based on our formulae, we compare these simulated results with the analytically predicted mean and variances of the repertoire overlap. Complementing the results on simulated repertoires, we derive explicit expressions for the richness and its uncertainty for specific, single-parameter truncated power-law probability distributions. Finally, the information loss associated with grouping together certain receptor sequences, as is done in spectratyping, is also evaluated. Our approach can be, in principle, applied under more general and mechanistically realistic clone generation models.
Diversifying science, technology, engineering, and mathematics (STEM) requires a critical examination of institutional structures at every educational level. In higher education, performance in core introductory courses required for STEM degrees is strongly associated with degree completion. Leveraging a multi-institutional database, we examine nearly 110,000 student records from six large, public, research-intensive universities in order to assess whether these introductory courses disproportionately weed out underrepresented minority (URM) students. We find that the association between low performance in an introductory STEM class and failure to obtain a STEM degree is stronger for URM students than for other students, even after controlling for academic preparation in high school and intent to obtain a STEM degree. To facilitate interpretation of our multivariate logistic regression model, and to highlight the dire situation in higher education, we also calculate predicted probabilities of STEM degree attainment for students of various demographics. The probability of obtaining a STEM degree for a STEM-intending white male student with average academic preparation who receives grades of C or better in all introductory courses is 48%. In contrast, for an otherwise similar URM female student, the probability is merely 35%. If these students receive less than a C in even one introductory STEM course, the probabilities drop to 33% and 21%, respectively.
Diversifying the future cancer research workforce requires that students engage in cancer research, persist in paths toward science, technology, engineering, mathematics, and medicine (STEMM) fields, and choose cancer research careers. The Summer Clinical Oncology Research Experience (SCORE) Program at Memorial Sloan Kettering, designed in 2010 to engage undergraduate (U) and post-baccalaureate (PB) students from diverse backgrounds in cancer research, is an 8-week summer program pairing an U or PB student with a faculty mentor to conduct cancer research. We report demographics and career paths for 2010-2019 SCORE students. Of 116 students, 112 (97%) attended public universities, and 75 (64%) were in their first 2 years of college. Race/ethnicity was Black/African American, 20 (17%); Hispanic/Latinx, 15 (13%); multiracial, five (4%); Asian, 40 (34%); White/Caucasian, 36 (31%). A total of 112 (97%) identified as female; 47 (41%) were first-generation college students, and 85 (73%) were from immigrant families. As of 2021, 114 (98%) persisted in paths toward STEMM careers: 44 (38%) medical school (MS) students, 14 (12%) residents, two (2%) practicing physicians, 12 (10%) pursuing non-MD STEMM advanced degrees, 21 (18%) working in non-MD STEMM fields, 17 (15%) applying to MS, and 4 (3%) U science majors. Cancer research participation significantly increased from 5% pre- to 84% post-SCORE. A total of 63/116 (54%) students subsequently co-authored 152 peer-reviewed publications, including 105 (69%) in oncology. SCORE engaged underrepresented U and PB students in cancer research, and 98% of these students persisted in paths toward STEMM careers. Long-term follow-up is needed to assess the enduring engagement of these underrepresented students in cancer research.