Yoxas/statistical_literacy huggingface dataset:
Lady health workers (LHWs) provide lifesaving maternal and child health services to >60% of Pakistan's population but are poorly compensated and overburdened. Moreover, LHWs' training does not incorporate efforts to nurture attributes necessary for equitable and holistic healthcare delivery. We developed an interdisciplinary humanities curriculum, deriving its strengths from local art and literature, to enhance character virtues such as empathy and connection, interpersonal communication skills, compassion and purpose among LHWs. We tested the curriculum's feasibility and impact to enhance character strengths among LHWs.We conducted a multiphase mixed-methods pilot study in two towns of Karachi, Pakistan. We delivered the humanities curriculum to 48 LHWs via 12 weekly sessions, from 15 June to 2 September 2021. We developed a multiconstruct character strength survey that was administered preintervention and postintervention to assess the impact of the training. In-depth interviews were conducted with a subset of randomly selected participating LHWs.Of 48 participants, 47 (98%) completed the training, and 34 (71%) attended all 12 sessions. Scores for all outcomes increased between baseline and endline, with highest increase (10.0 points, 95% CI 2.91 to 17.02; p=0.006) observed for empathy/connection. LHWs provided positive feedback on the training and its impact in terms of improving their confidence, empathy/connection and ability to communicate with clients. Participants also rated the sessions highly in terms of the content's usefulness (mean: 9.7/10; SD: 0.16), the success of the sessions (mean: 9.7/10; SD: 0.17) and overall satisfaction (mean: 8.2/10; SD: 3.3).A humanities-based training for front-line health workers is a feasible intervention with demonstrated impact of nurturing key character strengths, notably empathy/connection and interpersonal communication. Evidence from this study highlights the value of a humanities-based training, grounded in local literature and cultural values, that can ultimately translate to improved well-being of LHWs thus contributing to better health outcomes among the populations they serve.
Land subsidence risk assessment (LSRA) is a multi-attribute decision analysis (MADA) problem and is often characterized by both quantitative and qualitative attributes with various types of uncertainty. Therefore, the problem needs to be modeled and analyzed using methods that can handle uncertainty. In this article, we propose an integrated assessment model based on the evidential reasoning (ER) algorithm and fuzzy set theory. The assessment model is structured as a hierarchical framework that regards land subsidence risk as a composite of two key factors: hazard and vulnerability. These factors can be described by a set of basic indicators defined by assessment grades with attributes for transforming both numerical data and subjective judgments into a belief structure. The factor-level attributes of hazard and vulnerability are combined using the ER algorithm, which is based on the information from a belief structure calculated by the Dempster-Shafer (D-S) theory, and a distributed fuzzy belief structure calculated by fuzzy set theory. The results from the combined algorithms yield distributed assessment grade matrices. The application of the model to the Xixi-Chengnan area, China, illustrates its usefulness and validity for LSRA. The model utilizes a combination of all types of evidence, including all assessment information--quantitative or qualitative, complete or incomplete, and precise or imprecise--to provide assessment grades that define risk assessment on the basis of hazard and vulnerability. The results will enable risk managers to apply different risk prevention measures and mitigation planning based on the calculated risk states.
Language and the ability to communicate effectively are key factors in mental health and well-being. Despite this critical importance, research on language is limited by the lack of a scalable phenotyping toolkit. Here, we describe and showcase Lingo - a flexible online battery of language and nonverbal reasoning skills based on seven widely used tasks (COWAT, picture narration, vocal rhythm entrainment, rapid automatized naming, following directions, sentence repetition, and nonverbal reasoning). The current version of Lingo takes approximately 30 minutes to complete, is entirely open source, and allows for a wide variety of performance metrics to be extracted. We asked > 1,300 individuals from multiple samples to complete Lingo, then investigated the validity and utility of the resulting data. We conducted an exploratory factor analysis across 14 features derived from the seven assessments, identifying five factors. Four of the five factors showed acceptable test-retest reliability (Pearson's R > 0.7). Factor 2 showed the highest reliability (Pearson's R = 0.95) and loaded primarily on sentence repetition task performance. We validated Lingo with objective measures of language ability by comparing performance to gold-standard assessments: CELF-5 and the VABS-3. Factor 2 was significantly associated with the CELF-5 "core language ability" scale (Pearson's R = 0.77, p-value < 0.05) and the VABS-3 "communication" scale (Pearson's R = 0.74, p-value < 0.05). Factor 2 was positively associated with phenotypic and genetic measures of socieconomic status. Interestingly, we found the parents of children with language impairments had lower Factor 2 scores (p-value < 0.01). Finally, we found Lingo factor scores were significantly predictive of numerous psychiatric and neurodevelopmental conditions. Together, these analyses support Lingo as a powerful platform for scalable deep phenotyping of language and other cognitive abilities. Additionally, exploratory analyses provide supporting evidence for the heritability of language ability and the complex relationship between mental health and language.
Language is learned in complex social settings where listeners must reconstruct speakers' intended meanings from context. To navigate this challenge, children can use pragmatic reasoning to learn the meaning of unfamiliar words. A critical challenge for pragmatic reasoning is that it requires integrating multiple information sources, which have typically been studied separately. Here we study this integration process. First, we experimentally isolate two sources of pragmatic information: expectations about informative communication and common ground. Next, we use a probabilistic model of conversational reasoning to formalize how these information sources should be combined and how this process might develop. We use this model to generate quantitative predictions, which we test against new experimental data from 3- to 5-year-old children (N = 243) and adults (N = 694). Results show close alignment between model predictions and data. Furthermore, the model provided a better explanation of the data compared with simpler alternative models assuming that participants selectively ignore one information source. This work integrates distinct sets of findings regarding information sources for early language learning and suggests that pragmatic reasoning models can provide a quantitative framework for understanding developmental changes in language learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Language-appropriate care is critical for equitable, high-quality health care, but educational standards to assure graduate medical trainees are prepared to give such care are lacking. Detailed guidance for graduate medical education is provided by the Accreditation Council for Graduate Medical Education through the following: (1) an assessment framework for competencies, subcompetencies, and milestones for trainees and (2) the Clinical Learning Environment Review (CLER) Pathways for assessment of trainees' learning environments. These tools do not include a robust framework to evaluate trainees' abilities to offer language-appropriate care. They also do not address the learning environment's potential to support such care. A multidisciplinary group of linguistic, medical, and educational experts drafted a new subcompetency with milestones and an expanded CLER Pathway to highlight the importance of equitable care for patients who prefer languages other than English. These resources offer residency and fellowship programs tools to guide assessment, curriculum development, and learning-environment improvements related to language-appropriate care. Recognizing that programs have unique needs and resources, we propose a range of initial actions to address language equity. A focus on language diversity in the learning environment can have a broad and lasting impact on care quality, patient safety, and health equity.
Language, communication and understanding of information are central to safe, ethical and efficient maternity care. The National Health Service (NHS) commissioning board, NHS England, describes how healthcare providers should obtain language support through professionally trained interpreters. Providers of interpreters are commissioned to deliver remote/face to face interpretation across the NHS. Services can be booked in advance or calls can be made in real time. However, women report infrequent use of professionally trained interpreters during their maternity care, often relying on friends and family as interpreters which can compromise confidentiality, disclosure and accuracy. To determine the demand for, and provision of, professionally trained interpreters in practice, we sent a Freedom of Information (FOI) request to 119 NHS Trusts delivering maternity services in England in November 2022. For the financial years 2020/2021 and 2021/2022, we asked how many women in the maternity service were identified as needing an interpreter, the number and mode of interpreter sessions, and the annual spend on interpreting services. Data were analysed using descriptive statistics. One hundred maternity Trusts responded by 21st April 2023 (response rate 100/119-84 %). Of these, 56 (56 %) recorded a woman's need for an interpreter. Nineteen Trusts relied on documentation in paper notes and 37 Trusts recorded the information on a digital system. From the 37 Trusts where this information could be digitally retrieved, women requiring interpreter support reflected between 1 and 25 % of the annual birth rate of the Trust (average 9 %) and received an average of three interpreter sessions across their pregnancy, birth and postnatal journey. Telephone was the dominant mode used for interpreting sessions, though 11 Trusts favoured face to face interpreting. Financial spend on interpreting services varied across Trusts; some funded their own in-house interpreting services, or worked with local community groups in addition to their contracted interpreting provider. Information obtained from this FOI request suggests that documentation of a woman's interpreting need is not complete or consistent across NHS maternity services. As a result, it is not clear how many women require an interpreter, the mode of provision or how frequently it is provided, and the cost involved. However, the limited information available suggests a failure to provide interpreter support to women at each scheduled care encounter. This raises questions about, the risk of women not understanding the care being offered, and the increased risk of uninformed, unconsented care as women traverse pregnancy and birth. There appears to be failure to provide interpreter support to women at each scheduled maternity care encounter increasing the risk of uninformed, unconsented care.
Laparoscopic operations have become an indispensable therapeutic measure in surgical treatment due to the emerging trends of minimal invasiveness and precision in the field of surgery. Laparoscopic skills have gradually become part of the essential skills for young surgeons and hospitals at all levels are giving high priority to laparoscopic skills training. The innovation and development of simulative and virtual medical technology has given rise to effective ways and platforms for the training of laparoscopy surgeons in China. Based on the laparoscopy simulative virtual technology, our hospital gradually developed a systematic training and evaluation system for the laparoscopy training of surgical residents by offering theory courses on laparoscopy, conducting simulative and virtual systematic training, and developing assessment criteria for the training. Herein, we presented and shared our experience in applying laparoscopy simulative virtual technology in the training of surgical residents in order to promote the standardized residency training of laparoscopic skills in China and to provide reference for the implementation of standardized training of laparoscopic skills.
Laparoscopic pyloromyotomy (LP) for the treatment of infantile hypertrophic pyloric stenosis (IHPS) is a clean case with low expected rates of postoperative surgical site infection (SSI). Previous studies have shown a low risk of SSI following LP but also large variations in the utilization of prophylactic antibiotics. The goal of this study was to review the use of preoperative antibiotics for LP and to compare this with SSI incidence. We performed a retrospective single-center analysis of patients undergoing LP for infantile hypertrophic pyloric stenosis at a large quaternary children's hospital from January 2017 to June 2020. Subjects were <4 mo old. Exclusion criteria were those lost to follow-up within 30 d postoperatively and those who required open conversion intraoperatively. Statistical analysis was performed using Fisher's exact test, two-tailed independent t-tests, and descriptive statistics. Two-hundred twenty-seven patients were included, mean population age was 5.7 wk, and 81.1% were male. Preoperative antibiotics were administered in 39% of patients. Only 1.3% (n = 3) of all patients developed an SSI within 30 d of their operation. Analysis between patients who received preoperative antibiotics and those who did not revealed no difference in age (5.72 wk versus 5.72 wk, t (225) = 0.38, P = 0.70), sex (41% of males versus 32% of females, P = 0.39), length of stay (t(225) = -0.94, P = 0.35), or postoperative SSI (1.1% versus 1.4%, P > 0.999). Large variability was noted in antibiotic utilization by surgeon. In patients undergoing LP, there was no difference in SSI rates whether or not patients received preoperative antibiotics and, there is large variation in utilization. Measures are needed to decrease usage of prophylactic antibiotics before LP.
Laparoscopic surgery demands high precision and skill, necessitating effective training protocols that account for factors such as hand dominance. This study investigates the impact of hand dominance on the acquisition and proficiency of laparoscopic surgical skills, utilizing a novel assessment method that combines Network Models and electromyography (EMG) data. Eighteen participants, comprising both medical and non-medical students, engaged in laparoscopic simulation tasks, including peg transfer and wire loop tasks. Performance was assessed using Network Models to analyze EMG data, capturing muscle activity and learning progression. The NASA Task Load Index (TLX) was employed to evaluate subjective task demands and workload perceptions. Our analysis revealed significant differences in learning progression and skill proficiency between dominant and non-dominant hands, suggesting the need for tailored training approaches. Network Models effectively identified patterns of skill acquisition, while NASA-TLX scores correlated with participants' performance and learning progression, highlighting the importance of considering both objective and subjective measures in surgical training. The study underscores the importance of hand dominance in laparoscopic surgical training and suggests that personalized training protocols could enhance surgical precision, efficiency, and patient outcomes. By leveraging advanced analytical techniques, including Network Models and EMG data analysis, this research contributes to optimizing clinical training methodologies, potentially revolutionizing surgical education and improving patient care.
Large cohort studies examining trends in cancer-related suicide are lacking. We analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database, encompassing a total of 4,870,410 patients diagnosed with cancer from 1975 to 2017 in the United States. Joinpoint regression was used to estimate the annual percent change (APC) and average annual percentage change (AAPC) of age-adjusted rates of suicide. In the past 40 years, we revealed a gradual increase in cancer-related suicide rates from 1975 to 1989, followed by a gradual decrease from 1989 to 2013, and a marked decrease from 2013 to 2017. These trends suggested the potential impact of advancements in psychosocial care for patients with cancer in contributing to the observed decrease in suicide rates.
Large display environments are highly suitable for immersive analytics. They provide enough space for effective co-located collaboration and allow users to immerse themselves in the data. To provide the best setting-in terms of visualization and interaction-for the collaborative analysis of a real-world task, we have to understand the group dynamics during the work on large displays. Among other things, we have to study, what effects different task conditions will have on user behavior. In this paper, we investigated the effects of task conditions on group behavior regarding collaborative coupling and territoriality during co-located collaboration on a wall-sized display. For that, we designed two tasks: a task that resembles the information foraging loop and a task that resembles the connecting facts activity. Both tasks represent essential sub-processes of the sensemaking process in visual analytics and cause distinct space/display usage conditions. The information foraging activity requires the user to work with individual data elements to look into details. Here, the users predominantly occupy only a small portion of the display. In contrast, the connecting facts activity requires the user to work with the entire information space. Therefore, the user has to overview the entire display. We observed 12 groups for an average of 2 h each and gathered qualitative data and quantitative data in the form of surveys, field notes, video recordings, tracking data, and system logs. During data analysis, we focused specifically on participants' collaborative coupling (in particular, collaboration tightness, coupling styles, user roles, and task subdivision strategies) and territorial behavior. Our results both confirm and extend findings from the previous tabletop and wall-sized display studies. We could detect that participants tended to subdivide the task to approach it, in their opinion, in a more effective way, in parallel. We describe the subdivision strategies for both task conditions. We also detected and described multiple user roles, as well as a new coupling style that does not fit in either category: loosely or tightly. Moreover, we could observe a territory type that has not been mentioned previously in research. In our opinion, this territory type can affect the collaboration process of groups with more than two collaborators negatively. Finally, we investigated critical display regions in terms of ergonomics. We could detect that users perceived some regions as less comfortable for long-time work. The findings can be valuable for groupware interface design and development of group behavior models for analytical reasoning and decision making.
Large introductory lecture courses are frequently post-secondary students' first formal interaction with science, technology, engineering, and mathematics (STEM) disciplines. Grade outcomes in these courses are often disparate across student populations, which, in turn, has implications for student retention. This study positions such disparities as a manifestation of systemic inequities along the dimensions of sex, race/ethnicity, income, and first-generation status and investigates the extent to which they are similar across peer institutions. We examined grade outcomes in a selected set of early STEM courses across six large, public, research-intensive universities in the United States over ten years. In this sample of more than 200,000 STEM course enrollments, we find that course grade benefits increase significantly with the number of systemic advantages students possess at all six institutions. The observed trends in academic outcomes versus advantage are strikingly similar across universities despite the fact that we did not control for differences in grading practices, contexts, and instructor and student populations. The findings are concerning given that these courses are often students' first post-secondary STEM experiences. STEM course grades are typically lower than those in other disciplines; students taking them often pay grade penalties. The systemic advantages some student groups experience are correlated with significant reductions in these grade penalties at all six institutions. The consistency of these findings across institutions and courses supports the claim that inequities in STEM education are a systemic problem, driven by factors that go beyond specific courses or individual institutions. Our work provides a basis for the exploration of contexts where inequities are exacerbated or reduced and can be used to advocate for structural change within STEM education. To cultivate more equitable learning environments, we must reckon with how pervasive structural barriers in STEM courses negatively shape the experiences of marginalized students. The online version contains supplementary material available at 10.1186/s40594-024-00474-7.
Large language models (LLMs) have demonstrated tremendous capabilities in solving complex tasks, from quantitative reasoning to understanding natural language. However, LLMs sometimes suffer from confabulations (or hallucinations), which can result in them making plausible but incorrect statements1,2. This hinders the use of current large models in scientific discovery. Here we introduce FunSearch (short for searching in the function space), an evolutionary procedure based on pairing a pretrained LLM with a systematic evaluator. We demonstrate the effectiveness of this approach to surpass the best-known results in important problems, pushing the boundary of existing LLM-based approaches3. Applying FunSearch to a central problem in extremal combinatorics-the cap set problem-we discover new constructions of large cap sets going beyond the best-known ones, both in finite dimensional and asymptotic cases. This shows that it is possible to make discoveries for established open problems using LLMs. We showcase the generality of FunSearch by applying it to an algorithmic problem, online bin packing, finding new heuristics that improve on widely used baselines. In contrast to most computer search approaches, FunSearch searches for programs that describe how to solve a problem, rather than what the solution is. Beyond being an effective and scalable strategy, discovered programs tend to be more interpretable than raw solutions, enabling feedback loops between domain experts and FunSearch, and the deployment of such programs in real-world applications.