Family members’ experiences and management of resident-to-resident abuse in long-term residential care

ABSTRACT

Objectives: Resident-to-resident (RRA) abuse is increasingly recognized as a significant problem in long-term residential care.Families have a constant presence in this setting, yet their inclusion in research about RRA is minimal. The purpose of this study was to examine family members’ experiences and management of RRA. Methods: The methodology was critical ethnography.Twelve family members participated in in-depth interviews and 56 hours of participant observation were conducted. Data were analyzed thematically. Results: The main themes illuminate families’ experiences of RRA in a context that largely normalizes it.In the absence of formal supports, families developed a range of management strategies, ranging from passive to active intervention.Organizational factors, such as staffing levels and mix, and the physical environment also contributed to RRA. Discussion: Families are actively managing RRA in long-term residential care. Policies and programs, including educational supports, should be developed to validate and support families.     

Influence of a Community Health Worker Intervention on Mental Health Outcomes among Low-Income Latino and African American Adults with Type 2 Diabetes

This study investigated the influence of a community health worker (CHW) diabetes lifestyle intervention on mental health outcomes. Our study was guided by the principles of community-based participatory research. Data were collected from 164 African American (N = 94) and Hispanic adults (N = 70) participating in a randomized, 6-month delayed intervention group design for improving glycemic control. The intervention time periods were baseline to 6 months for the treatment group and 6–12 months for the delayed group. Linear mixed models were used to conduct longitudinal analyses of the Problem Areas in Diabetes (PAID) and Patient Health Questionnaire (PHQ) scales. In the model adjusted for demographics, the PAID dropped significantly from pre-intervention to post-intervention within both the treatment and delayed groups (p < 0.05) with an average intervention effect of ?6.4 (p < 0.01). The PAID dropped even further within the immediate group from 6 to 12 months. Although the PHQ did not change significantly, the PHQ-2 had an average intervention effect of ?0.3 (p < 0.05) in the model adjusted for demographics. This study contributes to an understanding of how a CHW-led diabetes intervention can result in positive mental health outcomes for Latinos and African Americans with Type 2 diabetes. It also highlights the importance of further exploring what factors may contribute to racial/ethnic variation in mental health outcomes for African Americans and Latinos with diabetes and the role CHWs can play.     

The Equality of the Ordinary Least Squares Estimator and the Best Linear Unbiased Estimator

It is well known that the ordinary least squares estimator of Xβ in the general linear model E _y = Xβ, cov y = σ² V, can be the best linear unbiased estimator even if V is not a multiple of the identity matrix. This article presents, in a historical perspective, the development of the several conditions for the ordinary least squares estimator to be best linear unbiased. Various characterizations of these conditions, using generalized inverses and orthogonal projectors, along with several examples, are also given. In addition, a complete set of references is provided.     

Ineffectiveness,Poor Coordination,and Corruption in Humanitarian Aid: The Syrian Refugee Crisis in Lebanon

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Corruption attracts scholar attention due to its harsh impact on people and institutions, especially in nonprofit...     

Effect of adding regressors on the equality of the BLUEs under two linear models

In this paper we consider the estimation of regression coefficients in two partitioned linear models, shortly denoted as , and , which differ only in their covariance matrices. We call and full models, and correspondingly, and small models. We give a necessary and sufficient condition for the equality between the best linear unbiased estimators (BLUEs) of X₁β₁ under and . In particular, we consider the equality of the BLUEs under the full models assuming that they are equal under the small models.     

Predicting online gambling self-exclusion: an analysis of the performance of supervised machine learning models

As gambling operators become increasingly sophisticated in their analysis of individual gambling behaviour, this study evaluates the potential for using machine learning techniques to identify individuals who used self-exclusion tools out of a sample of 845 online gamblers, based on analysing trends in their gambling behaviour. Being able to identify other gamblers whose behaviour is similar to those who decided to use self-exclusion tools could, for instance, be used to share responsible gaming messages or other information that aids self-aware gambling and reduces the risk of adverse outcomes. However, operators need to understand how accurate models can be and which techniques work well. The purpose of the article is to identify the most accurate technique out of four highly diverse techniques and to discuss how to deal analytically and practically with a rare event like self-exclusion, which was used by fewer than 1% of gamblers in our data-set. We conclude that balanced training data-sets are necessary for creating effective models and that, on our data-set, the most effective method is the random forest technique which achieves an accuracy improvement of 35 percentage points versus baseline estimates.     

Exploring normativity in disability studies

Normativity is a concept that is often misapplied in disability studies, especially in ‘postconventional’ accounts, where the concept is conflated with ‘normal’, ‘normate’, or ‘standard’. This article addresses this confusion, explores the meaning and use of ‘normativity’, and presents some analytic tools to discuss normative issues of right and wrong. The article finishes by discussing examples where conceptual confusions result in confused normative judgments focusing in particular on agency, responsibility and moral status. The article argues that disability research should carefully consider the use of theories and empirical knowledge in the light of their ethical implications as well as the lived experiences of disability.