Yoxas/statistical_literacy huggingface dataset:
Quality improvement (QI) projects often employ statistical process control (SPC) charts to monitor process or outcome measures as part of ongoing feedback, to inform successive Plan-Do-Study-Act cycles and refine the intervention (formative evaluation). SPC charts can also be used to draw inferences on effectiveness and generalisability of improvement efforts (summative evaluation), but only if appropriately designed and meeting specific methodological requirements for generalisability. Inadequate design decreases the validity of results, which not only reduces the chance of publication but could also result in patient harm and wasted resources if incorrect conclusions are drawn. This paper aims to bring together much of what has been written in various tutorials, to suggest a process for using SPC in QI projects. We highlight four critical decision points that are often missed, how these are inter-related and how they affect the inferences that can be drawn regarding effectiveness of the intervention: (1) the need for a stable baseline to enable drawing inferences on effectiveness; (2) choice of outcome measures to assess effectiveness, safety and intervention fidelity; (3) design features to improve the quality of QI projects; (4) choice of SPC analysis aligned with the type of outcome, and reporting on the potential influence of other interventions or secular trends.These decision points should be explicitly reported for readers to interpret and judge the results, and can be seen as supplementing the Standards for Quality Improvement Reporting Excellence guidelines. Thinking in advance about both formative and summative evaluation will inform more deliberate choices and strengthen the evidence produced by QI projects.
Quality improvement interventions have been part of the national agenda aimed at reducing maternal and neonatal morbidities and mortality. Despite different interventions, neonatal mortality and morbidity rates remain steady. This review aimed to map and synthesize the evidence of maternal and newborn quality improvement interventions in Ethiopia. A scoping review was reported based on the reporting items for systematic reviews and meta-analysis extensions for the scoping review checklist. Data extraction, collation, and organization were based on the Joanna Briggs Institute manual of the evidence synthesis framework for a scoping review. The maternal and neonatal care standards from the World Health Organization and the Donabedian quality of health framework were used to summarize the findings. Nineteen articles were included in this scoping review. The review found that the studies were conducted across various regions of Ethiopia, with the majority published after 2013. The reviewed studies mainly focused on three maternal care quality interventions: mobile and electronic health (eHealth), quality improvement standards, and human resource mobilization. Moreover, the reviewed studies explored various approaches to quality improvement, such as providing training to healthcare workers, health extension workers, traditional birth attendants, the community health development army, and mothers and supplying resources needed for maternal and newborn care. In conclusion, quality improvement strategies encompass community involvement, health education, mHealth, data-driven approaches, and health system strengthening. Future research should focus on the impact of physical environment, culture, sustainability, cost-effectiveness, and long-term effects of interventions. Healthcare providers' knowledge, skills, attitudes, satisfaction, and adherence to guidelines should also be considered.
Quality improvement is an international priority, and quality education and training are important parts of hospital quality management. The aim of this study was to understand the knowledge, attitudes and practices (KAP) and its influencing factors related to quality training in medical staff. A questionnaire survey was conducted by convenience sampling to assess the KAP of quality training in Taizhou Enze Medical Center. Principal component analysis was used to extract factors from the questionnaire. Descriptive statistics (frequency, median, mean), Kendall grade correlation analysis, and Mann-Whitney U tests were used to analyze the data. A total of 205 staff members participated in the questionnaire survey. For the 5 factors of the KAP scale, the highest score was factor F4, recognition and support for quality training (mean = 90.55, median = 100), followed by factor F3, perceived benefits (mean = 84.46, median = 85.65). Relatively lower scores were found for factor F2, quality knowledge learning and mastery (mean = 63.09, median = 63.89), and F5, quality management practices and sharing (mean = 82.07, median = 75.00). There was a correlation between the 5 factors. The scores of F2 (quality knowledge learning and mastery) for staff with senior professional titles were higher than those for staff with intermediate professional titles or below. The score of F3 (perceived benefits of quality training) in medical technicians and nurses was higher than in doctors and administrative personnel. Our findings showed that the respondents' attitude toward quality training was positive, but their knowledge mastery and practice behaviors should be further improved. Occupational category and professional title were the influencing factors of the quality training KAP. Therefore, hospital should conduct quality management training at a wider scope according to the competency requirements of different groups, and further optimize the improvement and innovation system.
Quality improvement is now a central tenet in physiotherapy care, and quality indicators (QIs), as measurable elements of care, have been applied to analyze and evaluate the quality of physiotherapy care over the past two decades. QIs, based on Donabedian's model of quality of care, provide a foundation for measuring (improvements in) quality of physiotherapy care, providing insight into the many remaining evidentiary gaps concerning diagnostics, prognostics and treatment, as well as patient-related outcome measures. In this overview we provide a synthesis of four recently published articles from our project group on the topic of quantitative measures of quality improvement in physiotherapy care, in this context specifically focused on patients with WAD in primary care physiotherapy. A set of process and outcome QIs (n = 28) was developed for patients with WAD and linked to a database consisting of routinely collected data (RCD) on patients with WAD collected over a 16-year period. The QIs were then embedded per step of the clinical reasoning process: (a) administration (n = 2); (b) history taking (n = 7); (c) objectives of examination (n = 1); (d) clinical examination (n = 5); (e) analysis and conclusion (n = 1); (f) treatment plan (n = 3); (g) treatment (n = 2); (h) evaluation (n = 5); and (i) discharge (n = 2). QIs were expressed as percentages, allowing target performance levels to be defined ≥70% or ≤30%, depending on whether the desired performance required an initially high or low QI score. Using RCD data on primary care patients with WAD (N = 810) and a set of QIs, we found that the quality of physiotherapy care has improved substantially over a 16-year period. This conclusion was based on QIs meeting predetermined performance targets of ≥70% or ≤30%. Twenty-three indicators met the target criterium of ≥70% and three indicators ≤30%. Our recommended set of QIs, embedded in a clinical reasoning process for patients with WAD, can now be used as a basis for the development of a validated QI set that effectively measures quality (improvement) of primary care physiotherapy in patients with WAD.
Quality of life is determined by the lifestyle of individuals. If people have a healthy lifestyle, then they have a higher quality of life. What makes a person's lifestyle is the way he thinks. Therefore, if the thinking is healthy, the person's lifestyle will be healthy, and if the thinking is unhealthy, the person's lifestyle will also be damaged, which will reduce the quality of life. The research method was mixed method in two quantitative and qualitative phases. The research method in the qualitative phase was customary qualitative content analysis, in which the data analysis process was based on the approach of Granheim and Ladman. In the quantitative phase, the psychometric properties of the research scale were measured. This research was conducted from 2016 to 2021 in Iran. The statistical population in the qualitative section was all scientific texts in the form of articles, books and dissertations that were extracted from scientific databases such as ScienceDirect, PubMed, Elsevier, Ebsco, Sid, Magiran. Indicators of intellectual thinking have been mentioned as research results, which are the main components of the new model of thinking. Moreover, the level of thinking was mentioned and Cognitive distortions, cognitive bias, and perceptual errors were mentioned as sources of harm and error. Intellectual thinking has been mentioned as healthy thinking, which is the main factor of a health promotion lifestyle because healthy lifestyle improves the quality of life of people, which is one of the indicators of public health. Therefore, a higher quality of life can be achieved from the model of healthy thinking, which is a new perspective on promoting public health.
Quantitative biology is a rapidly advancing field in the biological sciences, particularly given the rise of large datasets and computer processing capabilities that have continually expanded over the past 50 years. Thus, the question arises, How should K-12 biology teachers incorporate quantitative biology skills into their biology curriculum? The teaching of quantitative biology has not been readily integrated into undergraduate biology curricula that impact preservice teachers. This has potential to cascade effects downward into the quality of learning about quantitative biology that can be expected in K-12 contexts. In this paper, we present the perspectives of a mathematics educator, a science educator, and two biologists, and discuss how we have personally incorporated aspects of quantitative reasoning into our courses. We identify some common challenges relevant to expanding implementation of quantitative reasoning in undergraduate biology courses in order to serve the needs of preservice teachers-both in their disciplinary courses and methods courses. For example, time constraints, math pedagogical content knowledge, and personal views about the relevance of quantitative principles in biology teaching and learning can impact how and to what extent they become implemented in curricula. In addition, although national standards at the K-12 level do address quantitative reasoning, the emphasis and guidance provided are sparser than for other content standards. We predict that both K-12 standards and guidelines for undergraduate education will only increase in their emphasis on quantitative skills as computation, "big data," and statistical modeling are increasingly becoming requisite skills for biologists.
Quantitative biology is dedicated to taking advantage of quantitative reasoning and advanced engineering technologies to make biology more predictable. Microfluidics, as an emerging technique, provides new approaches to precisely control fluidic conditions on small scales and collect data in high-throughput and quantitative manners. In this review, the authors present the relevant applications of microfluidics to quantitative biology based on two major categories (channel-based microfluidics and droplet-based microfluidics), and their typical features. We also envision some other microfluidic techniques that may not be employed in quantitative biology right now, but have great potential in the near future.
Quantitative microbial risk assessment (QMRA) is the current method of choice for determining the risk to human health from exposure to microorganisms of concern. However, current approaches are often constrained by the availability of required data, and may not be able to incorporate the many varied factors that influence this risk. Systems models, based on Bayesian networks (BNs), are emerging as an effective complementary approach that overcomes these limitations. This article aims to provide a comparative evaluation of the capabilities and challenges of current QMRA methods and BN models, and a scoping review of recent published articles that adopt the latter for microbial risk assessment. Pros and cons of systems approaches in this context are distilled and discussed. A search of the peer-reviewed literature revealed 15 articles describing BNs used in the context of QMRAs for foodborne and waterborne pathogens. These studies were analysed in terms of their application, uses and benefits in QMRA. The applications were notable in their diversity. BNs were used to make predictions, for scenario assessment, risk minimisation, to reduce uncertainty and to separate uncertainty and variability. Most studies focused on a segment of the exposure pathway, indicating the broad potential for the method in other QMRA steps. BNs offer a number of useful features to enhance QMRA, including transparency, and the ability to deal with poor quality data and support causal reasoning. The method has significant untapped potential to describe the complex relationships between microbial environmental exposures and health.
Quantitative proteomics has enhanced our capability to study protein dynamics and their involvement in disease using various techniques, including statistical testing, to discern the significant differences between conditions. While most focus is on what is different between conditions, exploring similarities can provide valuable insights. However, exploring similarities directly from the analyte level, such as proteins, genes, or metabolites, is not a standard practice and is not widely adopted. In this study, we propose a statistical framework called QuEStVar (Quantitative Exploration of Stability and Variability through statistical hypothesis testing), enabling the exploration of quantitative stability and variability of features with a combined statistical framework. QuEStVar utilizes differential and equivalence testing to expand statistical classifications of analytes when comparing conditions. We applied our method to an extensive data set of cancer cell lines and revealed a quantitatively stable core proteome across diverse tissues and cancer subtypes. The functional analysis of this set of proteins highlighted the molecular mechanism of cancer cells to maintain constant conditions of the tumorigenic environment via biological processes, including transcription, translation, and nucleocytoplasmic transport.
Quantitative reasoning (QR) skills have become a critical competency for undergraduate biology students, and recommendations for curricular reform urge QR training throughout undergraduate biology programs. Much research has been directed at course design, pedagogy, and student challenges in QR, but less research has been directed toward understanding how biology faculty conceptualize the QR skills they are called upon to teach. We conducted in-depth, semistructured interviews with 15 participants teaching introductory biology courses to learn how faculty conceptualize QR at the introductory level. Using phenomenology, responses were coded to establish inductive codes. We found that two themes emerged from the coded conceptualizations: sophisticated, cognitively complex QR skills and basic QR skills. Participants placed emphasis on the more complex QR skills as being important in the undergraduate curriculum, beginning at the introductory level. Participants' conceptualizations of QR aligned with skills called for in curriculum reform, but the perceived notion of "basic" for some skills may not align with the literature. This suggests that more is needed in aligning faculty conceptualization of QR with curriculum, pedagogy, and assessment.
Quantitative reasoning and techniques are increasingly ubiquitous across the life sciences. However, new graduate researchers with a biology background are often not equipped with the skills that are required to utilize such techniques correctly and efficiently. In parallel, there are increasing numbers of engineers, mathematicians, and physical scientists interested in studying problems in biology with only basic knowledge of this field. Students from such varied backgrounds can struggle to engage proactively together to tackle problems in biology. There is therefore a need to establish bridges between those disciplines. It is our proposal that the beginning of graduate school is the appropriate time to initiate those bridges through an interdisciplinary short course. We have instigated an intensive 10-day course that brought together new graduate students in the life sciences from across departments within the National University of Singapore. The course aimed at introducing biological problems as well as some of the quantitative approaches commonly used when tackling those problems. We have run the course for three years with over 100 students attending. Building on this experience, we share 11 quick tips on how to run such an effective, interdisciplinary short course for new graduate students in the biosciences.
Quantitative reasoning is one of the core competencies identified as a priority for transforming the undergraduate biology curriculum. However, first-year biology majors often lack confidence in their quantitative skills. We revised an introductory biology lab to emphasize the teaching of basic laboratory calculations, utilizing multiple teaching tools, including online prelab quizzes, minilab lectures, calculation worksheets, and online video tutorials. In addition, we implemented a repetitive assessment approach whereby three types of basic calculations-unit conversions, calculating molar concentrations, and calculating dilutions-were assessed on all quizzes and exams throughout the semester. The results showed that learning improved for each of the three quantitative problem types assessed and that these learning gains were statistically significant, both from first assessment to midterm and, notably, from midterm to final. Additionally, the most challenging problem type for students, calculating molar concentrations, showed the greatest normalized learning gains in the second half of the semester. The latter result suggests that persistent assessment resulted in continued learning even after formal, in-class teaching of these approaches had ended. This approach can easily be applied to other courses in the curriculum and, given the learning gains achieved, could provide a powerful means to target other quantitative skills.
Quantitative research literacy, including matching variables with the appropriate statistical tests, is a key element in counselor education and preparation. Counselor educators are tasked with teaching quantitative research and statistics, which counselors-in-training tend to find anxiety-producing. Authors aimed to provide a succinct overview of matching variables with appropriate statistical tests and provide strategies counselor educators can use to enhance their pedagogy.
Quantitative skills are becoming central to the undergraduate and graduate curriculum in ecology and evolutionary biology. While previous studies acknowledge that students perceive their quantitative training to be inadequate, there is little guidance on best practices. Moreover, with the recent COVID-19 sudden transition to online learning, there is even less guidance on how to effectively teach quantitative ecology online. Here, I synthesize a prescription of pedagogical best practices for teaching quantitative ecology online based on a broad review of the literature on multiple quantitative disciplines. These best practices include the following: (1) design and implement the class to meet learning goals using online strategies specifically; (2) create an open, inclusive, and welcoming online environment that promotes a sense of learning community; (3) acknowledge the diversity of talents and learning strategies; (4) use real-world examples and assessments; (5) account for gaps in knowledge; (6) emphasize the modeling cycle process; (7) focus on developing ideas rather than tools or procedures; (8) if needed, introduce computational tools thoroughly before combining them with mathematical or statistical concepts; (9) evaluate the course constantly; and (10) put your heart and soul into the class. I hope these practices help fellow instructors of quantitative ecology facing similar challenges in providing our students with the knowledge and skills needed to meet the challenges of the future.
Quantitative studies have provided valuable statistical insights into Health-Related Quality of Life (HRQoL) among patients with Heart Failure (HF), yet they often lack the depth to fully capture the nuanced, subjective experiences of living with HF particularly in the specific context of Jordan. This study explores the personal narratives of HF patients to understand the full impact of HF on their daily lives, revealing HRQoL aspects that quantitative metrics often miss. This is crucial in developing regions, where the increasing prevalence of HF intersects with local healthcare practices, cultural views, and patient expectations, providing key insights for tailored interventions and better patient care. Utilizing a phenomenological qualitative design, this study conducted face-to-face semi-structured interviews with 25 HF patients to deeply explore their lived experiences. Thematic analysis was employed to identify major themes related to their perceptions of HF as a disease, its impact on various HRQoL domains, and their recommended strategies to enhance HRQoL. The study involved 25 participants (13 males, 12 females), aged 26-88 years (mean 63), with diverse education and heart failure (HF) severities. It revealed three themes: HF perceptions, its impact on health-related quality of life (HRQoL) across physical, psychosocial, spiritual, cognitive, and economic domains, and HRQoL improvement strategies. Participants had varied HF knowledge; some lacked basic understanding. The physical impact was most significant, affecting daily life and causing symptoms like breathing difficulties, coughing, edema, and fatigue. This physical aspect influenced their psychosocial and spiritual lives, cognitive functions, and economic stability, leading to fear, frustration, worry, social isolation, spiritual and cognitive challenges, and employment problems. The results underscores the need for holistic healthcare approaches, integrating medical, psychological, and social support. Key recommendations include integrated care models, comprehensive patient education, support networks, and policy interventions to enhance HF patient care.