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Magnetic field changes as earthquake precursors have been the subject of numerous studies and some controversy. Infrequent large earthquakes and sparse magnetometer coverage along fault zones complicate statistical analysis. We present an analysis of ground‐based magnetic time‐series measurements before 19 earthquakes ≥M4.5 in California drawing from over 330,000 site‐days of measurement spanning a decade. To perform a fair existential test for electromagnetic antecedents we applied a pre‐specified statistical analysis with two key ideas. First, we combine signals from nearby (≤40 km) sites via spectral cross‐power, and then look for large spikes in frequency domain (0.016–25 Hz). The former is only possible with a dense set of sites running over a long period of time. In this statistical case‐control study we used the machine learning concept of rigorously separated train and test sets of earthquakes which were generated via a rule‐based query of the USGS earthquake catalog. Before each declustered earthquake, we constructed one period 24–72 hr before (the "precursor" or "p‐period") and a series of seven equally‐sized preceding periods ("quiescent" or "q‐periods"). We distilled the data in each period to a frequency‐dependent feature—the 98th percentile of spectral cross power. We trained a model based on Linear Discriminant Analysis and applied the discriminator to the test set revealing a modest effect in the days leading up to an earthquake. While the observed effect size is not directly useful for earthquake prediction (long a scientific goal), it suggests a relationship which should be further investigated for a physical link. Plain Language Summary: We identified changes in the magnetic field near intermediate‐large earthquakes in California in the days before the earthquakes happened. The statistical signal is of modest size, which means that we can not directly provide a prediction that can be used to alert the public. This study provides evidence that there is a physical change that can be observed in the days before an earthquake, but further scientific study is needed to understand this process. Key Points: Frequency domain analysis of ground‐based magnetometer data shows a modest change in days leading up to intermediate‐large (M ≥ 4.5) earthquakesOne novel part of the analysis is the use of cross‐power signals, which combines the signals from instruments separated by tens of kmA supplementary analysis of the data with first order global geomagnetic effects subtracted increased the measured effect size significantly [ABSTRACT FROM AUTHOR]

Drawing on 25 in-depth interviews with parents employed in the service sector in the San Francisco Bay area, we find that meeting the demands of work and parenting almost invariably involved reliance on informal child care. We unpack the relationship between work schedules and specific constellations of informal child care. We show that the stability and predictability of work schedules shaped childcare arrangements. Working parents with stable, although frequently nonstandard, schedules often managed child care using a tag team parenting approach. Those with unstable schedules often engaged in a child-care scramble, in which care arrangements were pieced together on an ad hoc basis. Some parents with unstable work schedules avoided child-care instability by relying heavily on one family anchor who could consistently provide child care. On-call family support can sometimes buffer against the instability created by unstable and unpredictable work schedules, but instability in work schedules often reproduces instability at home. [ABSTRACT FROM AUTHOR]

Although some past studies suggest that candidates may receive more votes when their names are listed first among their competitors than when they are listed later, two recent studies challenged this conclusion with regard to major-party candidates running in statewide races and raised questions about the impact of analytic methods on the conclusions of name-order research. Using the largest data set to date—a set of quasi-randomized natural experiments involving 402 candidates running in 76 statewide California elections—this study tests a series of hypotheses about the conditions under which name-order effects are most likely. Regardless of the analytic method used, a small primacy effect appeared consistently that could have a substantive impact on some contests. This effect was larger in races for lower-visibility offices, in years with higher turnout, and in races that were not close. All of this is consistent with the claim that name-order effects occur among voters who have little or no information about the candidates or among voters who feel ambivalence about the candidates. [ABSTRACT FROM PUBLISHER]

Background: Testing for COVID-19 remains limited in the United States and across the world. Poor allocation of limited testing resources leads to misutilization of health system resources, which complementary rapid testing tools could ameliorate., Objective: To predict SARS-CoV-2 PCR positivity based on complete blood count components and patient sex., Study Design: A retrospective case-control design for collection of data and a logistic regression prediction model was used. Participants were emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing. 33 confirmed SARS-CoV-2 PCR positive and 357 negative patients at Stanford Health Care were used for model training. Validation cohorts consisted of emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing in Northern California (41 PCR positive, 495 PCR negative), Seattle, Washington (40 PCR positive, 306 PCR negative), Chicago, Illinois (245 PCR positive, 1015 PCR negative), and South Korea (9 PCR positive, 236 PCR negative)., Results: A decision support tool that utilizes components of complete blood count and patient sex for prediction of SARS-CoV-2 PCR positivity demonstrated a C-statistic of 78 %, an optimized sensitivity of 93 %, and generalizability to other emergency department populations. By restricting PCR testing to predicted positive patients in a hypothetical scenario of 1000 patients requiring testing but testing resources limited to 60 % of patients, this tool would allow a 33 % increase in properly allocated resources., Conclusions: A prediction tool based on complete blood count results can better allocate SARS-CoV-2 testing and other health care resources such as personal protective equipment during a pandemic surge., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2020 Elsevier B.V. All rights reserved.)