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This article outlines a five-phase process of qualitative analysis that draws on deductive (codes developed a priori) and inductive (codes developed in the course of the analysis) coding strategies, as well as guided memoing and analytic questioning, to support trustworthy qualitative studies. The five-phase process presented here can be used as a whole or in part to support researchers in planning, articulating, and executing systematic and transparent qualitative data analysis; developing an audit trail to ensure study dependability and trustworthiness; and/or fleshing out aspects of analysis processes associated with specific methodologies.

Background/Introduction Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. Methods The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. Results The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. Conclusions The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

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Background Breast cancer (BC) is a leading cause of cancer-related death in females worldwide. Previous studies have demonstrated that matrix metalloproteinases (MMPs) play key roles in metastasis and are associated with survival in various cancers. The prognostic values of MMP2 and MMP9 expression in BC have been investigated, but the results remain controversial. Thus, we performed the present meta-analysis to investigate the associations between MMP2/9 expressions in tumor cells with clinicopathologic features and survival outcome in BC patients. Methods Eligible studies were searched in PubMed, Web of Science, EMBASE, CNKI and Wanfang databases. The associations of MMP2/9 overexpression in tumor cells with overall survival (OS), disease-free survival (DFS) and recurrence-free survival (RFS) were assessed by hazard ratio (HR) and 95% confidence interval (CI). The associations of MMP2/9 overexpression with clinicopathological features were investigated by calculating odds ratio (OR) and 95% CI. Subgroup analysis, sensitivity analysis, meta-regression, and analysis for publication bias were performed. Results A total of 41 studies comprising 6517 patients with primary BC were finally included. MMP2 overexpression was associated with an unfavorable OS (HR = 1.60, 95% CI 1.33 –1.94, P  < 0.001) while MMP9 overexpression predicted a shorter OS (HR = 1.52, 95% CI 1.30 –1.77, P  < 0.001). MMP2 overexpression conferred a higher risk to distant metastasis (OR = 2.69, 95% CI 1.35–5.39, P  = 0.005) and MMP9 overexpression correlated with lymph node metastasis (OR = 2.90, 95% CI 1.86 – 4.53, P  < 0.001). Moreover, MMP2 and MMP9 overexpression were both associated with higher clinical stage and histological grade in BC patients. MMP9 overexpression was more frequent in patients with larger tumor sizes. Conclusions Tumoral MMP2 and MMP9 are promising markers for predicting the prognosis in patients with BC.

In a nationally representative sample, first-year U.S. college students \"somewhat agree,\" on average, that they feel like they belong at their school. However, belonging varies by key institutional and student characteristics; of note, racialethnic minority and first-generation students report lower belonging than peers at 4-year schools, while the opposite is true at 2-year schools. Further, at 4-year schools, belonging predicts better persistence, engagement, and mental health even after extensive covariate adjustment. Although descriptive, these patterns highlight the need to better measure and understand belonging and related psychological factors that may promote college students' success and well-being.

Education is a complex system, which has conceptual and methodological implications for education research and policy. In this article, an overview is first provided of the Complex Systems Conceptual Framework for Learning (CSCFL), which consists of a set of conceptual perspectives that are generally shared by educational complex systems, organized into two focus areas: collective behaviors of a system, and behaviors of individual agents in a system. Complexity and research methodologies for education are then considered, and it is observed that commonly used quantitative and qualitative techniques are generally appropriate for studying linear dynamics of educational systems. However, it is proposed that computational modeling approaches, being extensively used for studying nonlinear characteristics of complex systems in other fields, can provide a methodological complement to quantitative and qualitative education research approaches. Two research case studies of this approach are discussed. We conclude with a consideration of how viewing education as a complex system using complex systems' conceptual and methodological tools can help advance education research and also inform policy.

Informed by the theoretical lens of opportunity hoarding, this study considers whether STEM postsecondary fields stand apart via the disproportionate exclusion of Black and Latina/o youth. Utilizing national data from the Beginning Postsecondary Study (BPS), the authors investigate whether Black and Latina/o youth who begin college as STEM majors are more likely to depart than their White peers, either by switching fields or by leaving college without a degree, and whether patterns of departure in STEM fields differ from those in non-STEM fields. Results reveal evidence of persistent racial/ethnic inequality in STEM degree attainment not found in other fields.

Background Literature searches underlie the foundations of systematic reviews and related review types. Yet, the literature searching component of systematic reviews and related review types is often poorly reported. Guidance for literature search reporting has been diverse, and, in many cases, does not offer enough detail to authors who need more specific information about reporting search methods and information sources in a clear, reproducible way. This document presents the PRISMA-S (Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension) checklist, and explanation and elaboration. Methods The checklist was developed using a 3-stage Delphi survey process, followed by a consensus conference and public review process. Results The final checklist includes 16 reporting items, each of which is detailed with exemplar reporting and rationale. Conclusions The intent of PRISMA-S is to complement the PRISMA Statement and its extensions by providing a checklist that could be used by interdisciplinary authors, editors, and peer reviewers to verify that each component of a search is completely reported and therefore reproducible.

The study objective was to conduct a systematic review and meta-analysis on the proportion of asymptomatic infection among coronavirus disease 2019 (COVID-19) positive persons and their transmission potential. We searched Embase, Medline, bioRxiv, and medRxiv up to 22 June 2020. We included cohorts or cross-sectional studies which systematically tested populations regardless of symptoms for COVID-19, or case series of any size reporting contact investigations of asymptomatic index patients. Two reviewers independently extracted data and assessed quality using pre-specified criteria. Only moderate/high quality studies were included. The main outcomes were proportion of asymptomatic infection among COVID-19 positive persons at testing and through follow-up, and secondary attack rate among close contacts of asymptomatic index patients. A qualitative synthesis was performed. Where appropriate, data were pooled using random effects meta-analysis to estimate proportions and 95% confidence intervals (95% CI). Of 6,137 identified studies, 71 underwent quality assessment after full text review, and 28 were high/moderate quality and were included. In two general population studies, the proportion of asymptomatic COVID-19 infection at time of testing was 20% and 75%, respectively; among three studies in contacts it was 8.2% to 50%. In meta-analysis, the proportion (95% CI) of asymptomatic COVID-19 infection in obstetric patients was 95% (45% to 100%) of which 59% (49% to 68%) remained asymptomatic through follow-up; among nursing home residents, the proportion was 54% (42% to 65%) of which 28% (13% to 50%) remained asymptomatic through follow-up. Transmission studies were too heterogenous to meta-analyse. Among five transmission studies, 18 of 96 (18.8%) close contacts exposed to asymptomatic index patients were COVID-19 positive. Despite study heterogeneity, the proportion of asymptomatic infection among COVID-19 positive persons appears high and transmission potential seems substantial. To further our understanding, high quality studies in representative general population samples are required.

Five reanalysis datasets—National Centers for Environmental Prediction reanalysis II (NCEP-2), NCEP Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), Japanese 55-year Reanalysis Project (JRA-55), and National Aeronautics and Space Administration (NASA) Modern Era Reanalysis for Research and Applications Version-2 (MERRA-2)—are selected to estimate meteorological droughts of China using three drought indices—the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI). Drought indices, drought areas and drought severity estimated for China from these reanalysis datasets are assessed against corresponding results obtained from observed climate dataset of China using Nash–Sutcliffe efficiency (NSE), correlation coefficient, and the analysis of time series. Further, temperature, precipitation and potential evapotranspiration data of the five reanalysis datasets are also compared against the observed dataset. Drought indices and drought areas estimated from reanalysis datasets are generally more representative of historical droughts that had occurred in eastern China than in western China. However, the performance of these five reanalysis datasets in representing the drought severity is unsatisfactory in both western China and eastern China. SPEI is generally more representative than PDSI and SPI partly because temperature and potential evapotranspiration data of reanalysis datasets are generally better than precipitation data. PDSI is also based on a supply-and-demand model of soil moisture but estimating the demand of soil moisture is complicated. Therefore, SPEI is preferred over PDSI and SPI as the drought index to characterize the meteorological droughts of China. Climate data and meteorological drought characteristics of eastern China are best represented by JRA-55, while that of western China are best represented by MERRA-2. From 1980 to 2014, statistically significant increasing trends in annual drought areas and drought severity are detected from JRA-55 and observed climate datasets in eastern China, but they are only detected from observed dataset in western China.