Reducing Daily Hassles in the Classroom: Teaching Coping Techniques to Elementary School Children

Stress, including stress from daily hassles, can have a negative effect on children. Coping skills can be helpful for dealing with stress, but must be effective for the type of stressor the student is experiencing. Teaching children effective coping skills can help them manage stress and may also have a positive impact on perceived classroom climate. Researchers examined what the relation between a brief CBT intervention with a classroom-based generalization phase on the student rated frequency of daily hassles which occur at school and on the student rated distress levels associated with the hassles, how helpful and acceptable do the students find the intervention, and what the students’ perception of class climate were following the treatment relative to their pre-treatment perception of climate. Three elementary school third and fourth grade children struggling with daily hassles participated in a brief CBT intervention for developing coping skills. The study was constructed using a non-concurrent multiple baseline design. The results were somewhat mixed, but two of the students had fewer self-reported and teacher-reported hassles post-intervention. All of the students and their teachers reported that students were using a higher percentage of adaptive to maladaptive coping skills after the study. All three students also reported slight increases in their perception of classroom climate. Implications and future research are discusse

Implementing machine learning methods with complex survey data: Lessons learned on the impacts of accounting sampling weights in gradient boosting

Despite the prominent use of complex survey data and the growing popularity of machine learning methods in epidemiologic research, few machine learning software implementations offer options for handling complex samples. A major challenge impeding the broader incorporation of machine learning into epidemiologic research is incomplete guidance for analyzing complex survey data, including the importance of sampling weights for valid prediction in target populations. Using data from 15, 820 participants in the 1988-1994 National Health and Nutrition Examination Survey cohort, we determined whether ignoring weights in gradient boosting models of all-cause mortality affected prediction, as measured by the F1 score and corresponding 95% confidence intervals. In simulations, we additionally assessed the impact of sample size, weight variability, predictor strength, and model dimensionality. In the National Health and Nutrition Examination Survey data, unweighted model performance was inflated compared to the weighted model (F1 score 81.9% [95% confidence interval: 81.2%, 82.7%] vs 77.4% [95% confidence interval: 76.1%, 78.6%]). However, the error was mitigated if the F1 score was subsequently recalculated with observed outcomes from the weighted dataset (F1: 77.0%; 95% confidence interval: 75.7%, 78.4%). In simulations, this finding held in the largest sample size (N = 10,000) under all analytic conditions assessed. For sample sizes <5,000, sampling weights had little impact in simulations that more closely resembled a simple random sample (low weight variability) or in models with strong predictors, but findings were inconsistent under other analytic scenarios. Failing to account for sampling weights in gradient boosting models may limit generalizability for data from complex surveys, dependent on sample size and other analytic properties. In the absence of software for configuring weighted algorithms, post-hoc re-calculations of unweighted model performance using weighted observed outcomes may more accurately reflect model prediction in target populations than ignoring weights entirely

Heterogeneous trends in burden of heart disease mortality by subtypes in the United States, 1999-2018: observational analysis of vital statistics

Abstract Objective To describe trends in the burden of mortality due to subtypes of heart disease from 1999 to 2018 to inform targeted prevention strategies and reduce disparities. Design Serial cross sectional analysis of cause specific heart disease mortality rates using national death certificate data in the overall population as well as stratified by race-sex, age, and geography. Setting United States, 1999-2018. Participants 12.9 million decedents from total heart disease (49% women, 12% black, and 19% &lt;65 years old). Main outcome measures Age adjusted mortality rates (AAMR) and years of potential life lost (YPLL) for each heart disease subtype, and respective mean annual percentage change. Results Deaths from total heart disease fell from 752 192 to 596 577 between 1999 and 2011, and then increased to 655 381 in 2018. From 1999 to 2018, the proportion of total deaths from heart disease attributed to ischemic heart disease decreased from 73% to 56%, while the proportion attributed to heart failure increased from 8% to 13% and the proportion attributed to hypertensive heart disease increased from 4% to 9%. Among heart disease subtypes, AAMR was consistently highest for ischemic heart disease in all subgroups (race-sex, age, and region). After 2011, AAMR for heart failure and hypertensive heart disease increased at a faster rate than for other subtypes. The fastest increases in heart failure mortality were in black men (mean annual percentage change 4.9%, 95% confidence interval 4.0% to 5.8%), whereas the fastest increases in hypertensive heart disease occurred in white men (6.3%, 4.9% to 9.4%). The burden of years of potential life lost was greatest from ischemic heart disease, but black-white disparities were driven by heart failure and hypertensive heart disease. Deaths from heart disease in 2018 resulted in approximately 3.8 million potential years of life lost. Conclusions Trends in AAMR and years of potential life lost for ischemic heart disease have decelerated since 2011. For almost all other subtypes of heart disease, AAMR and years of potential life lost became stagnant or increased. Heart failure and hypertensive heart disease account for the greatest increases in premature deaths and the largest black-white disparities and have offset declines in ischemic heart disease. Early and targeted primary and secondary prevention and control of risk factors for heart disease, with a focus on groups at high risk, are needed to avoid these suboptimal trends beginning earlier in life. </jats:sec

Association of polymorphisms in genes encoding hormone receptors ESR1, ESR2 and LHCGR with the risk and clinical features of testicular germ cell cancer.

Testicular germ cell cancer (TGCC) is the most common malignancy in young men. Genetic variants known to be associated with risk of TGCC only partially account for the observed familial risks. We aimed to identify additional polymorphisms associated with risk as well as histological and clinical features of TGCC in 367 patients and 214 controls. Polymorphisms in ESR2 (rs1256063; OR=0.53, 95% CI: 0.35-0.79) and LHCGR (rs4597581; OR=0.68, 95% CI: 0.51-0.89, and rs4953617; OR=1.88, 95% CI: 1.21-2.94) associated with risk of TGCC. Polymorphisms in ESR1 (rs9397080; OR=1.85, 95% CI: 1.18-2.91) and LHCGR (rs7371084; OR=2.37, 95% CI: 1.26-4.49) associated with risk of seminoma and metastasis, respectively. SNPs in ESR1 (rs9397080) and LHCGR (rs7371084) were predictors of higher LH levels and higher androgen sensitivity index in healthy subjects. The results suggest that polymorphisms in ESR1, ESR2 and LHCGR contribute to the risk of developing TGCC, histological subtype, and risk to metastasis

Coincident current magnetic core memories

Call number: LD2668 .R4 1968 B38