Abstract:A deep understanding of how discrimination impacts psychological health and well-being of students could allow us to better protect individuals at risk and support those who encounter discrimination. While the link between discrimination and diminished psychological and physical well-being is well established, existing research largely focuses on chronic discrimination and long-term outcomes. A better understanding of the short-term behavioral correlates of discrimination events could help us to concretely qua… Show more
“…This performance was much better than the baseline model, which always predicted the population mean (indicated by the dashed red line). In addition, the model trained on only the Stability Index performed equally well as the current state-of-the-art model trained on the mean and standard deviations of the amount of each behavior 32,38,39 . Fig.…”
Section: Prediction Of Future Symptom Severitymentioning
Increased stability in one’s daily routine is associated with well-being in the general population and often a goal of behavioral interventions for people with serious mental illnesses like schizophrenia. Assessing behavioral stability has been limited in clinical research by the use of retrospective scales, which are susceptible to reporting biases and memory inaccuracies. Mobile passive sensors, which are less susceptible to these sources of error, have emerged as tools to assess behavioral patterns in a range of populations. The present study developed and examined a metric of behavioral stability from data generated by a passive sensing system carried by 61 individuals with schizophrenia for one year. This metric—the Stability Index—appeared orthogonal from existing measures drawn from passive sensors and matched the predictive performance of state-of-the-art features. Specifically, greater stability in social activity (e.g., calls and messages) were associated with lower symptoms, and greater stability in physical activity (e.g., being still) appeared associated with elevated symptoms. This study provides additional support for the predictive value of individualized over population-level data in psychiatric populations. The Stability Index offers also a promising tool for generating insights about the impact of behavioral stability in schizophrenia-spectrum disorders.
“…This performance was much better than the baseline model, which always predicted the population mean (indicated by the dashed red line). In addition, the model trained on only the Stability Index performed equally well as the current state-of-the-art model trained on the mean and standard deviations of the amount of each behavior 32,38,39 . Fig.…”
Section: Prediction Of Future Symptom Severitymentioning
Increased stability in one’s daily routine is associated with well-being in the general population and often a goal of behavioral interventions for people with serious mental illnesses like schizophrenia. Assessing behavioral stability has been limited in clinical research by the use of retrospective scales, which are susceptible to reporting biases and memory inaccuracies. Mobile passive sensors, which are less susceptible to these sources of error, have emerged as tools to assess behavioral patterns in a range of populations. The present study developed and examined a metric of behavioral stability from data generated by a passive sensing system carried by 61 individuals with schizophrenia for one year. This metric—the Stability Index—appeared orthogonal from existing measures drawn from passive sensors and matched the predictive performance of state-of-the-art features. Specifically, greater stability in social activity (e.g., calls and messages) were associated with lower symptoms, and greater stability in physical activity (e.g., being still) appeared associated with elevated symptoms. This study provides additional support for the predictive value of individualized over population-level data in psychiatric populations. The Stability Index offers also a promising tool for generating insights about the impact of behavioral stability in schizophrenia-spectrum disorders.
“…This analysis drew from a multi-year study focused on stress and well-being among college students [19]. For the past three years, undergraduate students were recruited to participate in a study for the duration of at least one academic term at a large public university in the Pacific Northwest (see Table 1 for term dates).…”
This mixed-method study examined the experiences of college students during the COVID-19 pandemic through surveys, experience sampling data collected over two academic quarters (Spring 2019 n1 = 253; Spring 2020 n2 = 147), and semi-structured interviews with 27 undergraduate students. There were no marked changes in mean levels of depressive symptoms, anxiety, stress, or loneliness between 2019 and 2020, or over the course of the Spring 2020 term. Students in both the 2019 and 2020 cohort who indicated psychosocial vulnerability at the initial assessment showed worse psychosocial functioning throughout the entire Spring term relative to other students. However, rates of distress increased faster in 2020 than in 2019 for these individuals. Across individuals, homogeneity of variance tests and multi-level models revealed significant heterogeneity, suggesting the need to examine not just means but the variations in individuals’ experiences. Thematic analysis of interviews characterizes these varied experiences, describing the contexts for students’ challenges and strategies. This analysis highlights the interweaving of psychosocial and academic distress: Challenges such as isolation from peers, lack of interactivity with instructors, and difficulty adjusting to family needs had both an emotional and academic toll. Strategies for adjusting to this new context included initiating remote study and hangout sessions with peers, as well as self-learning. In these and other strategies, students used technologies in different ways and for different purposes than they had previously. Supporting qualitative insight about adaptive responses were quantitative findings that students who used more problem-focused forms of coping reported fewer mental health symptoms over the course of the pandemic, even though they perceived their stress as more severe. These findings underline the need for interventions oriented towards problem-focused coping and suggest opportunities for peer role modeling.
“…The concept of bindings connects interactive parts of an ERC workflow with the underlying code and data. 170 erc.yml example fileSee the specification at https://o2r.info/erc-spec/. id : b9b0099e-9f8d-4a33-8acf-cb0c062efaec spec_version : 1 licenses : code : Apache-2.0 data : data-licenses.txt text : "Creative Commons Attribution 2.0 Generic (CC BY 2.0)" metadata : "see metadata license headers" …”
Section: Resultsmentioning
confidence: 99%
“…While Schema.org tags have been used for indexing data, to our knowledge there is no journal that supports, much less encourages, semantic markup of specific terms within a manuscript. There has been tacit support for such inline markup in newer manuscript composition tools such as Manubot 236 or the ERC's bindings, 170 but generally such terms could disambiguate concepts, point to the provenance of findings within a result section or from a figure, accelerate linked data and discovery, and improve understandability. Despite their significance, funding for the sustainable development and maintenance of core tools for research is scarce.…”
Summary
Reproducible computational research (RCR) is the keystone of the scientific method for
in silico
analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.
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