Yoxas/statistical_literacy huggingface dataset:
Effective stress management after unsuccessful cardiopulmonary resuscitation (CPR) requires the determination of perceived stress and identification and reinforcement of effective coping strategies. Therefore, the present study was conducted to determine perceived stress and coping strategies after unsuccessful CPR for the first time among Iranian pre-hospital emergency technicians. A cross-sectional, descriptive, correlational, multicenter study was conducted in 2022. The research population included all pre-hospital emergency technicians working in 44 urban, road, and aerial pre-hospital emergency stations in 22 counties of Ilam Province, west of Iran. A total of 374 technicians worked in these stations, which were included in the study by census method according to the inclusion criteria. The data collection tools were the Questionnaire of perceived stress after unsuccessful CPR and Brief Coping Orientation to Problems Experienced (COPE) Inventory. The collected data were analyzed with SPSS software version 27 using descriptive and analytical statistics (Pearson correlation test, multiple linear regression analysis, independent sample T-test, and ANOVA) at a significance level of p ≤ 0.05. The perceived stress was at a moderate level with a mean score of 65.56 ± 14.21. Among six coping strategies, problem-focused coping had the highest (3.01 ± 0.59) and denial had the lowest mean value (1.94 ± 0.79). Perceived stress had a significant direct relationship with 5 coping strategies, including problem-focused coping, support-focused coping, emotion-focused coping, self-distraction, and religion (p < 0.05). The strongest relationship was found between perceived stress and self-distraction followed by emotion-focused coping. According to the results of multiple regression analysis, emotion-focused coping and self-distraction had predictive roles. The participants used different coping strategies to cope with the stress of unsuccessful CPR. The most common coping mechanisms used by the participants were problem-focused, emotion-focused, and support-focused coping. Pre-hospital emergency officials can assist pre-hospital emergency technicians in coping with occupational stress, including the stress of unsuccessful CPR, through organizing educational courses, teaching effective coping strategies, and offering support programs.
Effective suicide risk assessments and interventions are vital for suicide prevention. Although assessing such risks is best done by health care professionals, people experiencing suicidal ideation may not seek help. Hence, machine learning (ML) and computational linguistics can provide analytical tools for understanding and analyzing risks. This, therefore, facilitates suicide intervention and prevention. This study aims to explore, using statistical analyses and ML, whether computerized language analysis could be applied to assess and better understand a person's suicide risk on social media. We used the University of Maryland Suicidality Dataset comprising text posts written by users (N=866) of mental health-related forums on Reddit. Each user was classified with a suicide risk rating (no, low, moderate, or severe) by either medical experts or crowdsourced annotators, denoting their estimated likelihood of dying by suicide. In language analysis, the Linguistic Inquiry and Word Count lexicon assessed sentiment, thinking styles, and part of speech, whereas readability was explored using the TextStat library. The Mann-Whitney U test identified differences between at-risk (low, moderate, and severe risk) and no-risk users. Meanwhile, the Kruskal-Wallis test and Spearman correlation coefficient were used for granular analysis between risk levels and to identify redundancy, respectively. In the ML experiments, gradient boost, random forest, and support vector machine models were trained using 10-fold cross validation. The area under the receiver operator curve and F1-score were the primary measures. Finally, permutation importance uncovered the features that contributed the most to each model's decision-making. Statistically significant differences (P<.05) were identified between the at-risk (671/866, 77.5%) and no-risk groups (195/866, 22.5%). This was true for both the crowd- and expert-annotated samples. Overall, at-risk users had higher median values for most variables (authenticity, first-person pronouns, and negation), with a notable exception of clout, which indicated that at-risk users were less likely to engage in social posturing. A high positive correlation (ρ>0.84) was present between the part of speech variables, which implied redundancy and demonstrated the utility of aggregate features. All ML models performed similarly in their area under the curve (0.66-0.68); however, the random forest and gradient boost models were noticeably better in their F1-score (0.65 and 0.62) than the support vector machine (0.52). The features that contributed the most to the ML models were authenticity, clout, and negative emotions. In summary, our statistical analyses found linguistic features associated with suicide risk, such as social posturing (eg, authenticity and clout), first-person singular pronouns, and negation. This increased our understanding of the behavioral and thought patterns of social media users and provided insights into the mechanisms behind ML models. We also demonstrated the applicative potential of ML in assisting health care professionals to assess and manage individuals experiencing suicide risk.
Effective teaching is key in preparing students to become successful evidence-based healthcare professionals. The effectiveness of graduate evidence-based practice (EBP) pedagogy is not often a subject of research studies. The purpose of this study was to determine how faculty from the 50 top graduate nursing schools in the United States perceived the effectiveness of EBP courses for graduate nursing students. A descriptive cross-sectional design was used to explore faculty perception of the effectiveness of EBP courses. A web-based survey was used for data collection. A total of 45 questionnaires were subjected to statistical analysis. The mean perception of the effectiveness of EBP courses for the whole sample, on a scale from 1 to 7, was 5.58 (min. 4.29; max. 6.73), a higher score signifying higher perceived effectiveness. The highest rated item concerned a school's access to different databases. The strongest correlations were found between the total score and the scores for items describing students' opportunities to strengthen and apply their EBP skills (rs = .66). The internal consistency of the Perception of Effectiveness of EBP Courses scale, based on standardized Cronbach's alpha, was .84, which signifies strong internal consistency. Faculty perceived themselves as most competent at the following EBP skills: (a) "Asking questions regarding patients' care" (6.56), (b) "Considering patient preferences when implementing EBP" (6.40), and (c) "Critically appraising the relevant body of evidence to address clinical questions" (6.40). To strengthen the effectiveness of EBP courses, students should have more opportunities to implement their EBP knowledge and skills after completing EBP courses. Evaluation of faculty perceptions of the effectiveness of EBP courses can help to guide the development of nursing school curricula that better integrate EBP. Further evaluation of the psychometric properties of the instrument used to measure perception of the effectiveness of EBP courses is required along with objective measures of faculty knowledge and skills in teaching EBP.
Effective vision screening programs for children require follow-up with eye care providers, but studies demonstrate poor follow-up rates after failed screenings. Programs have attempted to address this issue by providing free examinations and glasses after failed screenings. This study presents data from a mobile clinic program that provides instrument-based vision screening, eye examinations, and free glasses directly to children at school. Between 2018 and 2021, a nonprofit organization provided in-school instrument-based screening and noncycloplegic examinations and refractions in elementary, middle, and high schools. Deidentified screening and clinic data were reviewed retrospectively. Information about each school regarding total student enrollment, enrollment based on race, and number of economically disadvantaged students was obtained from the Virginia Department of Education. In the 2019-2020 school year, 14,006 students from 58 schools were screened, and 4,238 (30.3%) were referred. The percentage of economically disadvantaged students was positively correlated to referral rate (R2 = 0.406). Between 2019 and 2020, the mobile clinic examined 3,095 students from the same 58 schools. Of these, 72.9% were prescribed glasses, and 16.3% were referred for a more comprehensive eye examination and treatment. Students were also diagnosed with cataracts, nystagmus, amblyopia, and strabismus. A combined screening program and mobile clinic traveling directly to schools can provide a pipeline for delivering eye care to vulnerable populations.
Effectively assessing dental students' knowledge and skills is an important part of their education. Assessment techniques available to teachers, have become more diverse as new technologies are introduced. This study aimed to investigate what dental educators thought about current and future assessment techniques. This was a mixed method study; quantitative data being collected at an annual dental educators' conference, via on-screen questions requesting: i) demographic information; ii) type of student assessment currently used; iii) impact of Covid-19 on student assessment, and iv) future perceptions of assessment. Questions were posed in real time using the 'Vevox' platform. Descriptive statistical analysis was employed. Qualitative data were collected on 'post-its' and analysed thematically. 101 participants attended the study. 59.1% (n=60) had >11 years' experience. 64% (n=55) considered their summative and formative assessments were effective in developing student learning. Few participants reported students' involvement in creating their own assessments (n=8; 9.75%). 41.68% (n=36) participants agreed it was a good idea for students to be involved in developing assessments, but only 9.75% (n=8) reported it was already happening. Qualitative data indicated a reluctance to wholeheartedly embrace technology (including AI) when planning assessments. Most participants were not in favours of Co-creation or the use of some technologies. Participants, reflected on the challenges of adopting a universal assessment approach. Dental educators should be given the institutional support and autonomy to implement the best assessment methods for their students. Most of this cohort of experienced dental educators reported that assessments facilitated student learning. The role of assessment for dental students is essential as dental educators must ensure that dental students graduate with appropriate knowledge and technical skills to be safe when in general dental practice. The use of various assessment techniques needs to reflect the diversity of skills the students need to demonstrate.
Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust-especially among viewers with low graphical literacy. We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions. Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience.
Effectively designing risk information for the public is challenging and selecting the appropriate medium to deliver disease risk information is crucial. In a usability evaluation of a colon cancer risk website, we evaluated the public's understanding and interpretation of graphical displays of risk information (2 bar graphs). Results from this study suggest that many people do not understand risk and often misinterpret graphical displays of risk and associated terminology. This work shows the importance of not only including representational analysis within user-centered design of consumer health websites, but also evaluating the health and numerical literacy levels of these websites as an aspect of usability testing.
Effectively teaching scientific reasoning requires an understanding of the challenges students face when learning these skills. We designed an assessment that measures undergraduate student abilities to form hypotheses, design experiments, and interpret data from experiments in cellular and molecular biology. The assessment uses intermediate-constraint free-response questions with a defined rubric to facilitate use with large classes, while identifying common reasoning errors that may prevent students from becoming proficient at designing and interpreting experiments. The assessment measured a statistically significant improvement in a senior-level biochemistry laboratory course, and a larger improvement between the biochemistry lab students and a separate cohort in a first-year introductory biology lab course. Two common errors were identified for forming hypotheses and using experimental controls. Students frequently constructed a hypothesis that was a restatement of the observation it was supposed to explain. They also often made comparisons to control conditions not included in an experiment. Both errors were most frequent among first-year students, and decreased in frequency as students completed the senior-level biochemistry lab. Further investigation of the absent controls error indicated that difficulties with reasoning about experimental controls may be widespread in undergraduate students. The assessment was a useful instrument for measuring improvement in scientific reasoning at different levels of instruction, and identified errors that can be targeted to improve instruction in the process of science.
Efficacious programmes require implementation at scale to maximise their public health impact. TransformUs is an efficacious behavioural and environmental intervention for increasing primary (elementary) school children's (5-12 years) physical activity and reducing their sedentary behaviour within school and home settings. This paper describes the study protocol of a 5-year effectiveness-implementation trial to assess the scalability and effectiveness of the TransformUs programme. A type II hybrid implementation-effectiveness trial, TransformUs is being disseminated to all primary schools in the state of Victoria, Australia (n=1786). Data are being collected using mixed methods at the system (state government, partner organisations), organisation (school) and individual (teacher, parent and child) levels. Evaluation is based on programme Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework. RE-AIM domains are being measured using a quasi-experimental, pre/post, non-equivalent group design, at baseline, 12 and 24 months. Effectiveness will be determined in a subsample of 20 intervention schools (in Victoria) and 20 control schools (in New South Wales (NSW), Australia), at baseline, 12 and 24 months. Primary outcomes include TransformUs Reach, Adoption, Implementation and organisational Maintenance (implementation trial), and children's physical activity and sedentary time assessed using accelerometers (effectiveness trial). Secondary outcomes include average sedentary time and moderate to vigorous-intensity physical activity on weekdays and during school hours, body mass index z-scores and waist circumference (effectiveness trial). Linear mixed-effects models will be fitted to compare outcomes between intervention and control participants accounting for clustering of children within schools, confounding and random effects. The trial was approved by the Deakin University Human Research Ethics Committee (HEAG-H 28_2017), Victorian Department of Education, the NSW Department of Education, Australian Catholic University (2017-145R), Melbourne Archdiocese Catholic Schools and Catholic Schools NSW. Partners, schools/teachers and parents will provide an informed signed consent form prior to participating. Parents will provide consent for their child to participate in the effectiveness trial. Findings will be disseminated via peer-reviewed publications, scientific conferences, summary reports to schools and our partner organisations, and will inform education policy and practice on effective and sustainable ways to promote physical activity and reduce sedentary behaviours population-wide. Australian Clinical Trials Registration Registry (ACTRN12617000204347).
Efficient and precise surgical skills are essential in ensuring positive patient outcomes. By continuously providing real-time, data driven, and objective evaluation of surgical performance, automated skill assessment has the potential to greatly improve surgical skill training. Whereas machine learning-based surgical skill assessment is gaining traction for minimally invasive techniques, this cannot be said for open surgery skills. Open surgery generally has more degrees of freedom when compared to minimally invasive surgery, making it more difficult to interpret. In this paper, we present novel approaches for skill assessment for open surgery skills. We analyzed a novel video dataset for open suturing training. We provide a detailed analysis of the dataset and define evaluation guidelines, using state of the art deep learning models. Furthermore, we present novel benchmarking results for surgical skill assessment in open suturing. The models are trained to classify a video into three skill levels based on the global rating score. To obtain initial results for video-based surgical skill classification, we benchmarked a temporal segment network with both an I3D and a Video Swin backbone on this dataset. The dataset is composed of 314 videos of approximately five minutes each. Model benchmarking results are an accuracy and F1 score of up to 75 and 72%, respectively. This is similar to the performance achieved by the individual raters, regarding inter-rater agreement and rater variability. We present the first end-to-end trained approach for skill assessment for open surgery training. We provide a thorough analysis of a new dataset as well as novel benchmarking results for surgical skill assessment. This opens the doors to new advances in skill assessment by enabling video-based skill assessment for classic surgical techniques with the potential to improve the surgical outcome of patients.
Efficient coverage for newly developed vaccines requires knowing which groups of individuals will accept the vaccine immediately and which will take longer to accept or never accept. Of those who may eventually accept the vaccine, there are two main types: success-based learners, basing their decisions on others' satisfaction, and myopic rationalists, attending to their own immediate perceived benefit. We used COVID-19 vaccination data to fit a mechanistic model capturing the distinct effects of the two types on the vaccination progress. We proved the identifiability of the population proportions of each type and estimated that 47% of Americans behaved as myopic rationalists with a high variation across the jurisdictions, from 31% in Mississippi to 76% in Vermont. The proportion was correlated with the vaccination coverage, proportion of votes in favor of Democrats in 2020 presidential election, and education score.
Efficient methods for the extraction of features of interest remain one of the biggest challenges for the interpretation of cryo-electron tomograms. Various automated approaches have been proposed, many of which work well for high-contrast datasets where the features of interest can be easily detected and are clearly separated from one another. Our inner ear stereocilia cryo-electron tomographic datasets are characterized by a dense array of hexagonally packed actin filaments that are frequently cross-connected. These features make automated segmentation very challenging, further aggravated by the high-noise environment of cryo-electron tomograms and the high complexity of the densely packed features. Using prior knowledge about the actin bundle organization, we have placed layers of a highly simplified ball-and-stick actin model to first obtain a global fit to the density map, followed by regional and local adjustments of the model. We show that volumetric model building not only allows us to deal with the high complexity, but also provides precise measurements and statistics about the actin bundle. Volumetric models also serve as anchoring points for local segmentation, such as in the case of the actin-actin cross connectors. Volumetric model building, particularly when further augmented by computer-based automated fitting approaches, can be a powerful alternative when conventional automated segmentation approaches are not successful.
Efficient teamwork is crucial to provide optimal health care. This paper focuses on teamwork between Anaesthesiologists (ANES), Certified Registered Nurse Anaesthetists' (CRNA) and Critical Care Registered Nurses (CCRN) working in challenging environments such as the intensive care unit (ICU) and the operating room (OR). Conflicts are common between physicians and nurses, negatively impacting teamwork. Social hierarchies based on professional status and power inequalities between nurses and physicians plays a vital role in influencing teamwork. Foucault was a famous thinker especially known for his reasoning regarding power/knowledge. A Foucauldian perspective was therefore incorporated into this paper and the overall aim was to explore CCRN/CRNA perception of knowledge/power in teamwork with ANES.Methods A mixed-method approach was applied in this study. Data was collected using a web-based questionnaire containing both closed-end and open-ended questions. A total of 289 CCRNs and CRNAs completed the questionnaire. Data analysis was then conducted through five stages as outlined by Onwuebugzie and Teddlie; analysing quantitative data in SPSS 27.0 and qualitative data with a directed content analysis, finally merging data together in ATLAS.ti v.23.Results The result reveals a dissonance between quantitative and qualitative data; quantitative data indicates a well-functioning interdisciplinary teamwork between CCRN/CRNA and ANES - qualitative data highlights that there are several barriers and inequalities between the two groups. Medicine was perceived as superior to nursing, which was reinforced by both social and organisational structures at the ICU and OR.Conclusion Unconscious rules underlying current power structures in the ICU and OR works in favour of the ANES and biomedical paradigm, supporting medical knowledge. To achieve a more equal power distribution between CCRN/CRNAs and ANES, the structural hierarchies between nursing and medicine needs to be addressed. A more equal power balance between the two disciplines can improve teamwork and thereby reduce patient mortality and improve patient outcomes.