Schizophrenia and psychotic symptoms in families of two American Indian tribes

Abstract Background The risk of schizophrenia is thought to be higher in population isolates that have recently been exposed to major and accelerated cultural change, accompanied by ensuing socio-environmental stressors/triggers, than in dominant, mainstream societies. We investigated the prevalence and phenomenology of schizophrenia in 329 females and 253 males of a Southwestern American Indian tribe, and in 194 females and 137 males of a Plains American Indian tribe. These tribal groups were evaluated as part of a broader program of gene-environment investigations of alcoholism and other psychiatric disorders. Methods Semi-structured psychiatric interviews were conducted to allow diagnoses utilizing standardized psychiatric diagnostic criteria, and to limit cultural biases. Study participants were recruited from the community on the basis of membership in pedigrees, and not by convenience. After independent raters evaluated the interviews blindly, DSM-III-R diagnoses were assigned by a consensus of experts well-versed in the local cultures. Results Five of the 582 Southwestern American Indian respondents (prevalence = 8.6 per 1000), and one of the 331 interviewed Plains American Indians (prevalence = 3.02 per 1000) had a lifetime diagnosis of schizophrenia. The lifetime prevalence rates of schizophrenia within these two distinct American Indian tribal groups is consistent with lifetime expectancy rates reported for the general United States population and most isolate and homogeneous populations for which prevalence rates of schizophrenia are available. While we were unable to factor in the potential modifying effect that mortality rates of schizophrenia-suffering tribal members may have had on the overall tribal rates, the incidence of schizophrenia among the living was well within the normative range. Conclusion The occurrence of schizophrenia among members of these two tribal population groups is consistent with prevalence rates reported for population isolates and in the general population. Vulnerabilities to early onset alcohol and drug use disorders do not lend convincing support to a diathesis-stressor model with these stressors, commonly reported with these tribes. Nearly one-fifth of the respondents reported experiencing psychotic-like symptoms, reaffirming the need to examine sociocultural factors actively before making positive diagnoses of psychosis or schizophrenia.</p

A Multi-level Analysis of the Relationship between Instructional Practices and Retention in Computer Science

Increasing retention in computer science (CS) courses is a goal of many CS departments. A key step to increasing retention is to understand the factors that impact the likelihood students will continue to enroll in CS courses. Prior research on retention in CS has mostly examined factors such as prior exposure to programming and students’ personality characteristics, which are outside the control of undergraduate instructors. This study focuses on factors within the control of instructors, namely, instructional practices that directly impact students’ classroom experiences. Participants were recruited from 25 sections of 14 different courses over 4 semesters. A multi-level model tested the effects of individual and class-average perceptions of cooperative learning and teacher directedness on the probability of subsequent enrollment in a CS course, while controlling for students’ mastery of CS concepts and status as a CS major. Results indicated that students’ individual perceptions of instructional practices were not associated with retention, but the average rating of cooperative learning within a course section was negatively associated with retention. Consistent with prior research, greater mastery of CS concepts and considering or having declared a CS major were associated with a higher probability of taking a future CS courses. Implications for findings are discussed

The winter 2019 air pollution (PM2.5) measurement campaign in Christchurch, New Zealand

MAPM (Mapping Air Pollution eMissions) is a project whose goal is to develop a method to infer airborne particulate matter (PM) emissions maps from in situ PM concentration measurements. In support of MAPM, a winter field campaign was conducted in New Zealand in 2019 (June to September) to obtain the measurements required to test and validate the MAPM methodology. Two different types of instruments measuring PM were deployed: ES-642 remote dust monitors (17 instruments) and Outdoor Dust Information Nodes (ODINs; 50 instruments). The measurement campaign was bracketed by two intercomparisons where all instruments were co-located, with a permanently installed tapered element oscillating membrane (TEOM) instrument, to determine any instrument biases. Changes in biases between the pre-and post-campaign intercomparisons were used to determine instrument drift over the campaign period. Once deployed, each ES-642 was co-located with an ODIN. In addition to the PM measurements, meteorological variables (temperature, pressure, wind speed, and wind direction) were measured at three automatic weather station (AWS) sites established as part of the campaign, with additional data being sourced from 27 further AWSs operated by other agencies. Vertical profile measurements were made with 12 radiosondes during two 24 h periods and complimented measurements made with a mini micropulse lidar and ceilometer. Here we present the data collected during the campaign and discuss the correction of the measurements made by various PM instruments. We find that when compared to measurements made with a simple linear correction, a correction based on environmental conditions improves the quality of measurements retrieved from ODINs but results in over-fitting and increases the uncertainties when applied to the more sophisticated ES-642 instruments. We also compare PM2.5 and PM10 measured by ODINs which, in some cases, allows us to identify PM from natural and anthropogenic sources. The PM data collected during the campaign are publicly available from https://doi.org/10.5281/zenodo.4542559 (Dale et al., 2020b), and the data from other instruments are available from https://doi.org/10.5281/zenodo.4536640 (Dale et al., 2020a)