Help-seeking behaviors and attitudes of emerging adults: How college students reporting recent suicidal ideation utilize the internet compared to traditional resources

Abstract

Objective: Suicide is the second leading cause of death among emerging adults, yet little is known regarding their online help-seeking. Participants: The National Research Consortium of Counseling Centers in Higher Education's dataset (N=26,292). Methods: Students across the United States were recruited from 73 four-year institutions to participate during the 2010–2011 school year. Results: Observed the effectiveness of online and traditional help-seeking stratified by recent suicidal ideation related to their most distressful period in the past year. Females and younger students endorsed both types of help-seeking, yet those with recent ideation were less likely to disclose any help-seeking. Among those reporting recent ideation, only females reported that traditional supports were more than moderately helpful. Regardless of whether students endorsed ideation, students who were younger, engaged in risky behaviors and identified as females reported that online resources were more than moderately helpful. Conclusions: Online resources could potentially bridge barriers to traditional help-seeking for those reluctant to seek out care, yet traditional resources were reported to be the most effective among this sample.     

Towards congruence between teachers’ intentions and practice in intercultural education

ABSTRACT

The purpose of this study is to explore the relationship between teacher beliefs pertaining to cultural diversity and their actual teaching activities. Using a model-based approach, this study looks at beliefs regarding diversity issues at both the theoretical and empirical levels. At the theoretical level, we attempt to systematise the many meanings of ‘beliefs’ found in the literature. At the empirical level, we examine the link between theory, beliefs, and teaching activities relating to diversity, using integrated analysis tools. To accomplish this, we used a qualitative methodological approach to examine the relationship between theory and practice in education. The sample consisted of 45 in-service primary teachers. The first results showed a gap between what teachers tend to say and what they actually do. We argue that future research needs to focus on the effects of teacher beliefs on teacher practice, with special attention devoted to the reflective process.     

Increased risk of emergency department presentations for bronchiolitis in infants exposed to air pollution

Air pollution has been linked to an increased risk of several respiratory diseases in children, especially respiratory tract infections. The present study aims to evaluate the association between pediatric emergency department (PED) presentations for bronchiolitis and air pollution. PED presentations due to bronchiolitis in children aged less than 1 year were retrospectively collected from 2007 to 2018 in Padova, Italy, together with daily environmental data. A conditional logistic regression based on a time-stratified case-crossover design was performed to evaluate the association between PED presentations and exposure to NO₂, PM2.5, and PM10. Models were adjusted for temperature, relative humidity, atmospheric pressure, and public holidays. Delayed effects in time were evaluated using distributed lag non-linear models. Odds ratio for lagged exposure from 0 to 14 days were obtained. Overall, 2251 children presented to the PED for bronchiolitis. Infants’ exposure to higher concentrations of PM10 and PM2.5 in the 5 days before the presentation to the PED increased the risk of accessing the PED by more than 10%, whereas high concentrations of NO₂ between 2 and 12 days before the PED presentation were associated with an increased risk of up to 30%. The association between pollutants and infants who required hospitalization was even greater. A cumulative effect of NO₂ among the 2 weeks preceding the presentation was also observed. In summary, PM and NO₂ concentrations are associated with PED presentations and hospitalizations for bronchiolitis. Exposure of infants to air pollution could damage the respiratory tract mucosa, facilitating viral infections and exacerbating symptoms.     

A unifying view on some problems in probability and statistics

Statistical Methods & Applications - Let $$L$$ be a linear space of real random variables on the measurable space $$(\varOmega ,\mathcal {A})$$ . Conditions for the existence of a probability...     

Experiments with the Traveler��s Dilemma: welfare, strategic choice and implicit collusion

This article investigates behavior in the Traveler??s Dilemma game and isolates deviations from textbook predictions caused by differences in welfare perceptions and strategic miscalculations. It presents the results of an experimental analysis based on a 2?×?2 design where the own and the other subject??s bonus?Cpenalty parameters are changed independently. We find that the change in own bonus?Cpenalty alone entirely explains the effect on claims of a simultaneous change in one??s own and the other??s bonus?Cpenalty. An increase in the other subject??s bonus?Cpenalty has a significant negative effect on claims when the own bonus?Cpenalty is low, whereas it does not have a significant effect when the own bonus?Cpenalty is high. We also find that expected claims are inconsistent with actual claims in the asymmetric treatments. Focusing on reported strategies, we document substantial heterogeneity and show that changes in choices across treatments are largely explained by risk aversion.     

Improving the measurement of child welfare in the context of intra-household inequality

This study aims to illustrate how to improve the evaluation of child economic well-being and child poverty using the collective approach. International organisations such as the World Bank, OECD and UNICEF have made important efforts in recent years to produce reliable data and to measure poverty according to the most relevant items of household consumption baskets. However, when measuring poverty, it is common practice to assume either that resources are distributed equally within the household or that there are fixed equivalence scales (for example, a child can be counted as 0.5 adults). The methodology based on collective models of consumption overcomes this rigidity by allowing inequality within each household to be taken into account. The method assumes that each household devotes a fraction of resources to children, which can be estimated using the observed expenditure on child-exclusive goods. An example of how child poverty measures can be inaccurately estimated is illustrated through the case of young children in Albania.     

Modified quasi-profile likelihoods from estimating functions

We discuss higher-order adjustments for a quasi-profile likelihood for a scalar parameter of interest, in order to alleviate some of the problems inherent to the presence of nuisance parameters, such as bias and inconsistency. Indeed, quasi-profile score functions for the parameter of interest have bias of order O(1)$\mathrm{O}(1)$, and such bias can lead to poor inference on the parameter of interest. The higher-order adjustments are obtained so that the adjusted quasi-profile score estimating function is unbiased and its variance is the negative expected derivative matrix of the adjusted profile estimating equation. The modified quasi-profile likelihood is then obtained as the integral of the adjusted profile estimating function. We discuss two methods for the computation of the modified quasi-profile likelihoods: a bootstrap simulation method and a first-order asymptotic expression, which can be simplified under an orthogonality assumption. Examples in the context of generalized linear models and of robust inference are provided, showing that the use of a modified quasi-profile likelihood ratio statistic may lead to coverage probabilities more accurate than those pertaining to first-order Wald-type confidence intervals.     

Chain graph models: topological sorting of meta-arrows and efficient construction of $${\mathcal{B}}$$ -essential graphs

Essential graphs and largest chain graphs are well-established graphical representations of equivalence classes of directed acyclic graphs and chain graphs respectively, especially useful in the context of model selection. Recently, the notion of a labelled block ordering of vertices was introduced as a flexible tool for specifying subfamilies of chain graphs. In particular, both the family of directed acyclic graphs and the family of “unconstrained” chain graphs can be specified in this way, for the appropriate choice of . The family of chain graphs identified by a labelled block ordering of vertices is partitioned into equivalence classes each represented by means of a -essential graph. In this paper, we introduce a topological ordering of meta-arrows and use this concept to devise an efficient procedure for the construction of -essential graphs. In this way we also provide an efficient procedure for the construction of both largest chain graphs and essential graphs. The key feature of the proposed procedure is that every meta-arrow needs to be processed only once.     

Machine learning and design of experiments with an application to product innovation in the chemical industry

Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.     

M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.