Suchergebnisse

1. Linear Regression -- 2. Introduction to Multi-Level Regression -- 3. Two-Level Multi-Level Modeling -- 4. Higher-Level Multi-Level Modeling -- 5. Longitudinal Data Analysis -- 6. Nonlinear Regression Modeling -- 7. Nonlinear Mixed-Effects Modeling -- 8. Generalized Linear Model -- 9. Generalized Multi-Level Model for Dichotomous Outcome -- 10. Generalized Multi-Level Model for Counts Outcome.

PurposeWith organizations hiring from increasingly diverse labor markets, this study aims to examine the implications of newcomers' individual differentiation for their group identification. The paper proposes and tests a self-verification process in which individual differentiation predicts group identification through role innovation under positive social feedback on innovation (moderated mediation). Simultaneously, a self-categorization pathway is examined of the indirect negative influence of individual differentiation on group identification through role modeling (mediation).Design/methodology/approachSurvey data were collected at three time points from 161 UK university alumni.FindingsThe analyses support a self-verification pathway: newcomers with high individual differentiation report higher group identification via role innovation only when they receive positive feedback on their innovative actions. However, there was no support for a self-categorization pathway, with no indirect relationship found between individual differentiation and group identification via role modeling.Practical implicationsHR practitioners and managers who are responsible for helping newcomers adjust should consider newcomers' individual differentiation. Specifically, newcomers with high individual differentiation may more successfully navigate their transition and identify with their workgroup when given appropriate support, such as positive social feedback on their innovative actions.Originality/valueThe study extends organizational socialization research by focusing on when newcomers with high individual differentiation may experience group identification. The findings highlight the important role of positive social feedback on group identification; this suggests a potential means by which newcomers with high individual differentiation can settle successfully.

BACKGROUND: As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks. METHODS: In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time. FINDINGS: We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35–60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020. INTERPRETATION: News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions.

BACKGROUND : Many studies have modeled and predicted the spread of COVID-19 (coronavirus disease 2019) in the U.S. using data that begins with the first reported cases. However, the shortage of testing services to detect infected persons makes this approach subject to error due to its underdetection of early cases in the U.S. Our new approach overcomes this limitation and provides data supporting the public policy decisions intended to combat the spread of COVID-19 epidemic. METHODS : We used Centers for Disease Control and Prevention data documenting the daily new and cumulative cases of confirmed COVID-19 in the U.S. from January 22 to April 6, 2020, and reconstructed the epidemic using a 5-parameter logistic growth model. We fitted our model to data from a 2-week window (i.e., from March 21 to April 4, approximately one incubation period) during which large-scale testing was being conducted. With parameters obtained from this modeling, we reconstructed and predicted the growth of the epidemic and evaluated the extent and potential effects of underdetection. RESULTS : The data fit the model satisfactorily. The estimated daily growth rate was 16.8% overall with 95% CI: [15.95, 17.76%], suggesting a doubling period of 4 days. Based on the modeling result, the tipping point at which new cases will begin to decline will be on April 7th, 2020, with a peak of 32,860 new cases on that day. By the end of the epidemic, at least 792,548 (95% CI: [789,162, 795,934]) will be infected in the U.S. Based on our model, a total of 12,029 cases were not detected between January 22 (when the first case was detected in the U.S.) and April 4. CONCLUSIONS : Our findings demonstrate the utility of a 5-parameter logistic growth model with reliable data that comes from a specified period during which governmental interventions were appropriately implemented. Beyond informing public health decision-making, our model adds a tool for more faithfully capturing the spread of the COVID-19 epidemic. ; https://ghrp.biomedcentral.com ; hj2021 ...

Background: Psychosis is a mental disorder that, despite its low prevalence, causes high disease and economic burden. Inadequate knowledge, lack of confidence and stigmatising attitudes of healthcare professionals (HCPs) may lead to suboptimal care. Aim: To review the literature exploring HCPs' knowledge, confidence and attitudes in relation to psychosis care. Method: A systematic search was undertaken across three databases (MEDLINE, Embase, PsycINFO) using a search strategy encompassing the concepts: 'healthcare professionals', 'knowledge, attitude, and confidence in care' and 'psychotic illnesses and symptoms' to identify relevant records published from 1st January 2002 to 18th March 2022. Results were screened against predetermined inclusion and exclusion criteria by title and abstract, followed by full text. Data were extracted into tables and synthesised narratively. Results: Initially, 7,397 studies were identified. Following two-stage screening, 24 studies were eligible for inclusion. Of these studies, 16 explored attitudes, four explored knowledge and attitudes, one explored knowledge, one explored confidence, one explored attitudes and confidence in care and one explored all three constructs. Most HCPs in the included studies demonstrated stigmatising attitudes towards people with psychosis. Furthermore, certain HCPs, including nurses and general practitioners, demonstrated low levels of knowledge, while psychiatrists, occupational therapists, psychologists and nurses had low levels of confidence in caring for people with psychosis. Conversely, positive attitudes were also observed in some HCPs resulting from having acquaintances with lived experience of psychosis. The need for additional education and training to improve HCPs' knowledge and confidence in relation to caring for people living with psychosis was identified. Conclusions: Most attitudes identified were negative and stemmed from stigma, while some were positive due to HCPs' compassion and familiarity with psychosis. The level of knowledge and confidence identified were mostly suboptimal, and so further research is required to develop and evaluate tailored interventions to address this gap.