Yoxas/statistical_literacy huggingface dataset:
Responding well to clinical uncertainty is a crucial skill for any doctor. To better understand how medical students develop this skill, Social Cognitive Theory can be used to explore students' perceived capability to respond to situations of uncertainty. This study aimed to construct a self-efficacy questionnaire and use it to measure medical students' responses to clinical uncertainty. A 29-item questionnaire was constructed. For each item, participants rated their confidence in responding to uncertain situations using a scale of 0-100. Data were analysed with descriptive and inferential statistics. Aotearoa New Zealand. The questionnaire was distributed to 716 of 852 medical students in second, fourth and sixth year, at the three campuses of the Otago Medical School. The Self-Efficacy to Respond to Clinical Uncertainty (SERCU) questionnaire was completed by 495 participants (69% response rate) and found to be highly reliable (α=0.93). Exploratory factor analysis confirmed a unidimensional scale. A multiple linear regression model predicted self-efficacy scores from year of study, age, mode of entry, gender and ethnicity, F(11,470) = 4.252, p<0.001 adj. R²=0.069. Male students and those admitted to the programme 3 years postdegree or with significant allied health experience were predicted to have significantly higher self-efficacy scores. Year of study was not a significant predictor of average efficacy scores. Our research contributes a novel, highly reliable questionnaire that uses self-efficacy to measure medical student responses to uncertainty. The questionnaire revealed that students' confidence in responding to uncertainty may be more related to their background and life experience than to progression through the curriculum. Medical educators and researchers can use the SERCU questionnaire to obtain a new perspective on how their students respond to uncertainty, inform future research and tailor teaching about uncertainty.
Response Evaluation Criteria in Solid Tumors (RECIST) are commonly used to assess therapeutic response in clinical trials but not in routine care; thus, RECIST-based end points are difficult to include in observational studies. Clinician-anchored approaches for measuring clinical response have been validated but not widely compared with clinical trial data, limiting their use as evidence for clinical decision-making. To compare response- and progression-based end points in clinical trial and observational cohorts of patients with non-small cell lung cancer (NSCLC). This retrospective cohort study used patient-level data from the IMpower132 trial (conducted April 7, 2016, to May 31, 2017) and a nationwide electronic health record (EHR)-derived deidentified database (data collected January 1, 2011, to March 31, 2022). Patients in the observational cohort were selected according to the inclusion and exclusion criteria of the IMpower132 trial. All patients in the observational cohort had stage IV NSCLC. All patients were randomized to or received first-line carboplatin or cisplatin plus pemetrexed. End points included response rates, duration of response, and progression-free survival, compared between the trial and observational cohorts before and after weighting. Response rates for the observational cohort were derived from the EHR. A total of 769 patients met inclusion criteria, 494 in the observational cohort (median [IQR] age, 67 [60-74] years; 228 [46.2%] female; 45 [9.1%] Black or African American; 352 [71.3%] White; 53 [10.7%] American Indian or Alaska Native, Asian, Hawaiian or Pacific Islander, or multiracial) and 275 in the trial cohort (median [IQR] age, 63 [56-68] years; 90 [32.7%] female; 4 [1.5%] Black or African American; 194 [70.5%] White; 65 [23.6%] American Indian or Alaska Native, Asian, Hawaiian or Pacific Islander, or multiracial). All 3 end points were comparable between the study cohorts. Trial patients had a higher number of response assessments compared with patients in the weighted observational cohort. The EHR-derived response rate was numerically higher than the objective response rate after weighting (100.3 of 249.3 [40.2%] vs 105 of 275 [38.2%]) due to higher rates of observed partial response than RECIST-based partial response. Among patients with at least 1 response assessment, the EHR-derived response rate remained higher than the objective response rate (100.3 of 193.4 [51.9%] vs 105 of 256 [41.0%]) due to a higher proportion of patients in the observational cohort with no response assessment. In this study, response- and progression-based end points were similar between clinical trial and weighted observational cohorts, which increases confidence in the reliability of observational end points and can inform their interpretation in relation to trial end points. Additionally, the difference observed in response rates (including vs excluding patients with no response assessment) highlights the importance of future research adopting this 2-way approach when evaluating the relationship of EHR-derived and objective response rates.
Response process data collected from human-computer interactive items contain detailed information about respondents' behavioural patterns and cognitive processes. Such data are valuable sources for analysing respondents' problem-solving strategies. However, the irregular data format and the complex structure make standard statistical tools difficult to apply. This article develops a computationally efficient method for exploratory analysis of such process data. The new approach segments a lengthy individual process into a sequence of short subprocesses to achieve complexity reduction, easy clustering and meaningful interpretation. Each subprocess is considered a subtask. The segmentation is based on sequential action predictability using a parsimonious predictive model combined with the Shannon entropy. Simulation studies are conducted to assess the performance of the new method. We use a case study of PIAAC 2012 to demonstrate how exploratory analysis for process data can be carried out with the new approach.
Response process data from computer-based problem-solving items describe respondents' problem-solving processes as sequences of actions. Such data provide a valuable source for understanding respondents' problem-solving behaviors. Recently, data-driven feature extraction methods have been developed to compress the information in unstructured process data into relatively low-dimensional features. Although the extracted features can be used as covariates in regression or other models to understand respondents' response behaviors, the results are often not easy to interpret since the relationship between the extracted features, and the original response process is often not explicitly defined. In this paper, we propose a statistical model for describing response processes and how they vary across respondents. The proposed model assumes a response process follows a hidden Markov model given the respondent's latent traits. The structure of hidden Markov models resembles problem-solving processes, with the hidden states interpreted as problem-solving subtasks or stages. Incorporating the latent traits in hidden Markov models enables us to characterize the heterogeneity of response processes across respondents in a parsimonious and interpretable way. We demonstrate the performance of the proposed model through simulation experiments and case studies of PISA process data.
Response to intervention (RTI) is a method for providing academic support to students and for identifying specific learning disabilities (SLDs). Using interrupted time series and hazard models, we examined if statewide RTI adoption in the U.S. state of Tennessee was associated with changes in rates of SLD and first-time SLD identification in elementary schools, and if these associations varied across student groups. Response to intervention was associated with an initial decline in the percentage of students with SLD in the state that continued over time, with larger decreases for students who were Black or economically disadvantaged. Response to intervention was associated with a 61% average decrease in the odds of first-time SLD identification by third grade (about a 0.006 change in the predicted probability), with greater declines for students who were Black or economically disadvantaged. We discuss these results in the context of disproportionality in special education and the need for research examining whether declines in SLD were due to improved academic outcomes, as opposed to delays in identification.
Responsible citizenship and sound decision-making in today's information age necessitate an appreciation of the role of uncertainty in the process of generating data-based scientific knowledge. The latter calls for coordinating between different types of uncertainties, related to three types of relevant reasoning: statistical, scientific, and nature of science uncertainties. This article examines separately the uncertainties that young students articulate as they engage in activities designed to concurrently foster all three types of reasoning, and also explores how these different types can interrelate. The context of Citizen Science is particularly suited for this goal, providing a unique pedagogical opportunity for learning scientific content by engaging learners in authentic scientific practices, including data analysis. Based on literature from the three fields of statistics, science and nature of science education, we offer an integrative framework, Deterministic Relativistic and Middle ground (DReaM), which consists of nine sub-categories of uncertainty articulations. We utilize it to analyze an instrumental case study of a pair of middle school students' (ages 13 and 14) participation in a pilot study of an interdisciplinary extended learning sequence, as part of the Radon Citizen Science Project. The results of an interpretative microgenetic analysis identified all nine DReaM uncertainty articulations sub-categories. These are illustrated in the Findings section with key scenes from the pair's participation. The discussion depicts how these sub-categories manifested in this particular case study and suggests interrelations between them in a more extended depiction of the DReaM framework. We conclude with the pedagogical implications of the extended framework.
Resting state functional magnetic resonance imaging (R-fMRI) offers insight into how synchrony within and between brain networks is altered in disease states. Individual and disease-related variability in intrinsic connectivity networks may influence our interpretation of R-fMRI data. We used a personalized approach designed to account for individual variation in the spatial location of correlation maxima to evaluate R-fMRI differences between Parkinson's disease (PD) patients who showed cognitive decline, those who remained cognitively stable and cognitively stable controls. We compared fMRI data from these participant groups, studied at baseline and 18 months later, using both network-based statistics (NBS) and calculations of mean inter- and intra-network connectivity within pre-defined functional networks. The NBS analysis showed that PD participants who remained cognitively stable showed exclusively (at baseline) or predominantly (at follow-up) increased intra-network connectivity, whereas decliners showed exclusively reduced intra-network and inter- (ventral attention and default mode) connectivity, in comparison with the control group. Evaluation of mean connectivity between all regions of interest (ROIs) within a priori networks showed that decliners had consistently reduced inter-network connectivity for ventral attention, somatomotor, visual and striatal networks and reduced intra-network connectivity for ventral attention network to striatum and cerebellum. These findings suggest that specific functional connectivity covariance patterns differentiate PD cognitive subtypes and may predict cognitive decline. Further, increased intra and inter-network synchrony may support cognitive function in the face of PD-related network disruptions.
Resting-state eyes-closed electroencephalographic (rsEEG) alpha rhythms are dominant in posterior cortical areas in healthy adults and are abnormal in subjective memory complaint (SMC) persons with Alzheimer's disease amyloidosis. This exploratory study in 161 SMC participants tested the relationships between those rhythms and seed-based resting-state functional magnetic resonance imaging (rs-fMRI) connectivity between thalamus and visual cortical networks as a function of brain amyloid burden, revealed by positron emission tomography and cognitive reserve, measured by educational attainment. The SMC participants were divided into 4 groups according to 2 factors: Education (Edu+ and Edu-) and Amyloid burden (Amy+ and Amy-). There was a statistical interaction (p < 0.05) between the two factors, and the subgroup analysis using estimated marginal means showed a positive association between the mentioned rs-fMRI connectivity and the posterior rsEEG alpha rhythms in the SMC participants with low brain amyloidosis and high CR (Amy-/Edu+). These results suggest that in SMC persons, early Alzheimer's disease amyloidosis may contrast the beneficial effects of cognitive reserve on neurophysiological oscillatory mechanisms at alpha frequencies and connectivity between the thalamus and visual cortical networks.
Results of scientific experiments and research work, either conducted by individuals or organizations, are published and shared with scientific community in different types of scientific publications such as books, chapters, journals, articles, reference works and reference works entries. One aspect of these documents is their contents and the other is metadata. Metadata of scientific documents could be used to increase mutual cooperation, find people with common interest and research work, and to find scientific documents in the matching domains. The major issue in getting these benefits from metadata of scientific publications is availability of these data in unstructured (or semi-structured) format so that it can not be used to ask smart queries that can help in computing and performing different types of analysis on scientific publications data. Also, acquisition and smart processing of publications data is a complicated as well as time and resource consuming task. To address this problem we have developed a generic framework named as Linked Open Publications Data Framework (LOPDF). The LOPDF framework can be used to crawl, process, extract and produce machine understandable data (i.e., LOD) about scientific publications from different publisher specific sources such as portals, XML export and websites. In this paper we present the architecture, process and algorithm that we developed to process textual publications data and to produce semantically enriched data as RDF datasets (i.e., open data). The resulting datasets can be used to make smart queries by making use of SPARQL protocol. We also present the quantitative as well as qualitative analysis of our resulting datasets which ultimately can be used to compute the research behavior of organizations in rapidly growing knowledge society. Finally, we present the potential usage of producing and processing such open data of scientific publications and how results of performing smart queries on resulting open datasets can be used to compute the impact and perform different types of analysis on scientific publications data.
Resuscitation is a team effort, and it is increasingly acknowledged that team cooperation requires training. Staff shortages in many healthcare systems worldwide, as well as recent pandemic restrictions, limit opportunities for collaborative team training. To address this challenge, a learner-centred approach known as flipped learning has been successfully implemented. This model comprises self-directed, asynchronous pre-course learning, followed by knowledge application and skill training during in-class sessions. The existing evidence supports the effectiveness of this approach for the acquisition of cognitive skills, but it is uncertain whether the flipped classroom model is suitable for the acquisition of team skills. The objective of this study was to determine if a flipped classroom approach, with an online workshop prior to an instructor-led course could improve team performance and key resuscitation variables during classroom training. A single-centre, cluster-randomised, rater-blinded study was conducted on 114 final year medical students at a University Hospital in Germany. The study randomly assigned students to either the intervention or control group using a computer script. Each team, regardless of group, performed two advanced life support (ALS) scenarios on a simulator. The two groups differed in the order in which they completed the flipped e-learning curriculum. The intervention group started with the e-learning component, and the control group started with an ALS scenario. Simulators were used for recording and analysing resuscitation performance indicators, while professionals assessed team performance as a primary outcome. The analysis was conducted on the data of 96 participants in 21 teams, comprising of 11 intervention groups and 10 control groups. The intervention teams achieved higher team performance ratings during the first scenario compared to the control teams (Estimated marginal mean of global rating: 7.5 vs 5.6, p < 0.01; performance score: 4.4 vs 3.8, p < 0.05; global score: 4.4 vs 3.7, p < 0.001). However, these differences were not observed in the second scenario, where both study groups had used the e-learning tool. Flipped classroom approaches using learner-paced e-learning prior to hands-on training can improve team performance. German Clinical Trials Register ( https://drks.de/search/de/trial/DRKS00013096 ).
Retention in prevention of mother-to-child transmission of HIV programs is critical to reduce vertical transmission. To addresses challenges with retention, Mozambique launched a peer-support program in 2018, in which HIV-positive mothers provide adherence support as mentor mothers (MMs) for HIV-positive pregnant and lactating women and HIV-exposed and infected children. A descriptive qualitative evaluation was conducted across nine facilities in Gaza Province to assess the acceptability and barriers to implementation of the mentor mother program (MMP) among those receiving services and providing services. In-depth interviews and focus group discussions were conducted with MMs, MM supervisors, health care workers (HCWs), HIV-positive mothers enrolled in the MMP, HIV-positive mothers who declined MMP enrollment, and key informants involved in the implementation of the program. Thematic analysis identified emerging recurrent themes and patterns across the participants' responses. Data were collected between November-December 2020. There were initial challenges with acceptability of the MMP, especially regarding confidentiality concerns and MM roles. Sharing additional information about MMs and making small changes during the beginning of the MMP resulted in generally high acceptance of the MMP. HIV-positive mothers reported that counseling from MMs improved their understanding of the importance of anti-retroviral treatment (ART) and how to take and administer ART. HIV-positive mothers reported having reduced guilt and shame about their HIV-status, feeling less alone, and having more control over their health. MMs shared that their work made them feel valued and decreased their self-stigmatization. However, MMs also reported feeling that they had inadequate resources to perform optimal job functions; they listed inadequate transportation, insufficient stipends, and false addresses of clients among their constraints. Overall, HCWs felt that their workload was significantly reduced with MM support and wanted more MMs in the community and health facility. This study found that the MMP was considered a substantive and highly valued support to HIV-positive mothers, resulting in increased ART literacy among patients, improved self-reported well-being and sense of community and reduced feelings of isolation. Recommendations include strengthening MM training, increasing financial and materiel resources, additional information provided to newly enrolled mothers and support for the male partners.
Retention of nurses in long-term care (LTC) and home and community care (HCC) settings is a growing concern. Previous evidence underscores factors which contribute to nurses' intentions for retention in these sectors. However, perspectives of nursing students preparing to enter the workforce, and their intentions for short-term and long-term retention, remain unknown. This study aims to explore relationships between short-term and long-term intentions for retention with psychological empowerment, work engagement, career commitment, burnout, prosocial motivation, self-care and personal resilience among students enrolled in nursing educational bridging programs supported by the Bridging Educational Grant in Nursing (BEGIN) program in Ontario, Canada. This cross-sectional design study will use an open online survey to investigate perspectives of current nursing students enrolled in educational bridging programs on factors relating to psychological empowerment, work engagement, career commitment, resilience, burnout, prosocial motivation, self-care and intentions for retention. Additionally, the survey will collect demographic information, including age, gender, ethnicity, citizenship, income, family status, nursing role, and years of employment and/or education. Open-ended questions will elicit participants' perspectives on financial considerations for career planning and other factors impacting intentions for retention. Descriptive data will be presented for contextualisation of participants' demographic characteristics to enhance generalisability of the cohort. Descriptive statistics will be used to summarise participants' scores on various assessment measures, as well as their short-term and long-term intentions for retention in LTC and HCC after completion of BEGIN. A Pearson's product moment r correlation will determine relationships between intentions for retention and other measures, and linear regression will determine whether any potential correlations can be explained by regression. This research protocol received ethical approval from a research-intensive university research ethics board (#123211). Findings will be disseminated to nursing knowledge users in LTC and HCC through publications, conferences, social media and newsletters.