Urban American Indian Community Health Beliefs Associated with Addressing Cancer in the Northern Plains Region

American Indians residing in the Northern Plains region of the Indian Health Service experience some of the most severe cancer-related health disparities. We investigated ways in which the community climate among an American Indian population in an urban community in the Northern Plains region influences community readiness to address cancer. A Community Readiness Assessment, following the Community Readiness Model, conducted semi-structured interviews with eight educators, eight students, and eight community leaders from the American Indian community in Omaha’s urban American Indian population and established the Northern Plains region community at a low level of readiness to address cancer. This study reports on a subsequent qualitative study that analyzed all 24 interview transcriptions for emergent themes to help understand the prevailing attitude of the community toward cancer. A synthesis of six emergent themes revealed that the community’s perceptions of high levels of severity and barriers, paired with perceptions of low levels of susceptibility and benefits, lead to low levels of self-efficacy, all of which are reflected in minimal cues to action and little effort to address cancer. These findings, interpreted through the lens of the Health Belief Model, can inform the development of more community-based, comprehensive, and culturally appropriate approaches to address the multilevel determinants of health behaviors in relation to cancer among American Indians in the Northern Plains region

Examining the reproducibility of meta-analyses in psychology:A preliminary report

Meta-analyses are an important tool to evaluate the literature. It is essential that meta-analyses can easily be reproduced to allow researchers to evaluate the impact of subjective choices on meta-analytic effect sizes, but also to update meta-analyses as new data comes in, or as novel statistical techniques (for example to correct for publication bias) are developed. Research in medicine has revealed meta-analyses often cannot be reproduced. In this project, we examined the reproducibility of meta-analyses in psychology by reproducing twenty published meta-analyses. Reproducing published meta-analyses was surprisingly difficult. 96% of meta-analyses published in 2013-2014 did not adhere to reporting guidelines. A third of these meta-analyses did not contain a table specifying all individual effect sizes. Five of the 20 randomly selected meta-analyses we attempted to reproduce could not be reproduced at all due to lack of access to raw data, no details about the effect sizes extracted from each study, or a lack of information about how effect sizes were coded. In the remaining meta-analyses, differences between the reported and reproduced effect size or sample size were common. We discuss a range of possible improvements, such as more clearly indicating which data were used to calculate an effect size, specifying all individual effect sizes, adding detailed information about equations that are used, and how multiple effect size estimates from the same study are combined, but also sharing raw data retrieved from original authors, or unpublished research reports. This project clearly illustrates there is a lot of room for improvement when it comes to the transparency and reproducibility of published meta-analyses

The Reason for Prison: Americans Attitudes Towards Incarceration and the Effect of Learning about Its Consequences

Why do we send nonviolent offenders to prison when other types of penalties exist (e.g., House Arrest, Community Service, Probation)? Justifications for legal consequences at all typically involve: retribution, deterrence (specific or general), incapacitation, and rehabilitation. One desired consequence of incarceration is that the offender will not commit future crimes. Previous research, however, has shown that being incarcerated leads to higher rates of recidivism than facing alternate consequences for the same wrongful act. We utilize exploratory hypotheses to explore American adults' views of fair consequences for nonviolent marginal offenses, what people believe the purpose of prison is, and how that relates to the proclivity to invoke prison as a consequence. We investigate Americans’ attitudes towards different consequences for nonviolent wrongful acts that allow prison as one of several potential penalties (marginal incarcerable offenses). The null hypothesis states that reading the identified research will create a change in preference for prison as a punishment for nonviolent wrongful acts, and the alternative will reduce preference for prison as a punishment for nonviolent wrongful acts. We survey a demographically representative sample of 856 American adults about their preferred consequences for nonviolent offenses, and we also investigate how their preferences might differ after learning about research showing that incarceration leads to more, rather than fewer, future wrongful acts

Open Science: What, Why, and How

Open Science is a collection of actions designed to make scientific processes more transparent and results more accessible. Its goal is to build a more replicable and robust science; it does so using new technologies, altering incentives, and changing attitudes. The current movement towards open science was spurred, in part, by a recent “series of unfortunate events” within psychology and other sciences. These events include the large number of studies that have failed to replicate and the prevalence of common research and publication procedures that could explain why. Many journals and funding agencies now encourage, require, or reward some open science practices, including pre-registration, providing full materials, posting data, distinguishing between exploratory and confirmatory analyses, and running replication studies. Individuals can practice and encourage open science in their many roles as researchers, authors, reviewers, editors, teachers, and members of hiring, tenure, promotion, and awards committees. A plethora of resources are available to help scientists, and science, achieve these goals

Open Science: What, Why, and How

Open Science is a collection of actions designed to make scientific processes more transparent and results more accessible. Its goal is to build a more replicable and robust science; it does so using new technologies, altering incentives, and changing attitudes. The current movement towards open science was spurred, in part, by a recent “series of unfortunate events” within psychology and other sciences. These events include the large number of studies that have failed to replicate and the prevalence of common research and publication procedures that could explain why. Many journals and funding agencies now encourage, require, or reward some open science practices, including pre-registration, providing full materials, posting data, distinguishing between exploratory and confirmatory analyses, and running replication studies. Individuals can practice and encourage open science in their many roles as researchers, authors, reviewers, editors, teachers, and members of hiring, tenure, promotion, and awards committees. A plethora of resources are available to help scientists, and science, achieve these goals

Examining the reproducibility of meta-analyses in psychology: A preliminary report

Meta-analyses are an important tool to evaluate the literature. It is essential that meta-analyses can easily be reproduced to allow researchers to evaluate the impact of subjective choices on meta-analytic effect sizes, but also to update meta-analyses as new data comes in, or as novel statistical techniques (for example to correct for publication bias) are developed. Research in medicine has revealed meta-analyses often cannot be reproduced. In this project, we examined the reproducibility of meta-analyses in psychology by reproducing twenty published meta-analyses. Reproducing published meta-analyses was surprisingly difficult. 96% of meta-analyses published in 2013-2014 did not adhere to reporting guidelines. A third of these meta-analyses did not contain a table specifying all individual effect sizes. Five of the 20 randomly selected meta-analyses we attempted to reproduce could not be reproduced at all due to lack of access to raw data, no details about the effect sizes extracted from each study, or a lack of information about how effect sizes were coded. In the remaining meta-analyses, differences between the reported and reproduced effect size or sample size were common. We discuss a range of possible improvements, such as more clearly indicating which data were used to calculate an effect size, specifying all individual effect sizes, adding detailed information about equations that are used, and how multiple effect size estimates from the same study are combined, but also sharing raw data retrieved from original authors, or unpublished research reports. This project clearly illustrates there is a lot of room for improvement when it comes to the transparency and reproducibility of published meta-analyses