Religious Coping, Well-Being, and Denominational Affiliation among African-American Women

African-American women are considered one of the most religious cultural groups in the United States. Despite high levels of religiosity within this group, various stressors associated with experienced racism, sexism, and other life occurrences require coping as a method to endure negative experiences. While coping through religion is not uncommon, researchers sought to explore how different beliefs impacted the coping process among African-American women. This study investigated religious coping methods, well-being, and denominational affiliation of African-American women in three Protestant denominations where African Americans are highly represented: African Methodist Episcopal, Baptist (as governed by the National Baptist Convention, USA), and Church of God in Christ. The participant sample (N=202) was drawn from 14 churches in Houston, Texas and Bryan, Texas and ranged in age from 18 to 94. Participants completed surveys pertaining to demographics, religious coping methods, and current well-being. A confirmatory factor analysis was performed to justify variable creation for positive and negative religious coping methods; a regression analysis determined if religious coping methods and denominational affiliation affected well-being; and a moderating regression analysis and ANOVA F-tests were used to determine overall and individual denominational effects in the relationship between religious coping and well-being. Overall there was no relationship between religious coping, well-being, and denominational affiliation. No relationship was found between positive religious coping methods and well-being, but negative coping methods were associated with lower well-being. Also, there was no relationship found between religious coping methods and well-being and well-being and denominational affiliation

Religious Coping, Well-Being, and Denominational Affiliation among African-American Women

African-American women are considered one of the most religious cultural groups in the United States. Despite high levels of religiosity within this group, various stressors associated with experienced racism, sexism, and other life occurrences require coping as a method to endure negative experiences. While coping through religion is not uncommon, researchers sought to explore how different beliefs impacted the coping process among African-American women. This study investigated religious coping methods, well-being, and denominational affiliation of African-American women in three Protestant denominations where African Americans are highly represented: African Methodist Episcopal, Baptist (as governed by the National Baptist Convention, USA), and Church of God in Christ. The participant sample (N=202) was drawn from 14 churches in Houston, Texas and Bryan, Texas and ranged in age from 18 to 94. Participants completed surveys pertaining to demographics, religious coping methods, and current well-being. A confirmatory factor analysis was performed to justify variable creation for positive and negative religious coping methods; a regression analysis determined if religious coping methods and denominational affiliation affected well-being; and a moderating regression analysis and ANOVA F-tests were used to determine overall and individual denominational effects in the relationship between religious coping and well-being. Overall there was no relationship between religious coping, well-being, and denominational affiliation. No relationship was found between positive religious coping methods and well-being, but negative coping methods were associated with lower well-being. Also, there was no relationship found between religious coping methods and well-being and well-being and denominational affiliation

Religious Coping, Well-Being, and Denominational Affiliation among African-American Women

African-American women are considered one of the most religious cultural groups in the United States. Despite high levels of religiosity within this group, various stressors associated with experienced racism, sexism, and other life occurrences require coping as a method to endure negative experiences. While coping through religion is not uncommon, researchers sought to explore how different beliefs impacted the coping process among African-American women. This study investigated religious coping methods, well-being, and denominational affiliation of African-American women in three Protestant denominations where African Americans are highly represented: African Methodist Episcopal, Baptist (as governed by the National Baptist Convention, USA), and Church of God in Christ. The participant sample (N=202) was drawn from 14 churches in Houston, Texas and Bryan, Texas and ranged in age from 18 to 94. Participants completed surveys pertaining to demographics, religious coping methods, and current well-being. A confirmatory factor analysis was performed to justify variable creation for positive and negative religious coping methods; a regression analysis determined if religious coping methods and denominational affiliation affected well-being; and a moderating regression analysis and ANOVA F-tests were used to determine overall and individual denominational effects in the relationship between religious coping and well-being. Overall there was no relationship between religious coping, well-being, and denominational affiliation. No relationship was found between positive religious coping methods and well-being, but negative coping methods were associated with lower well-being. Also, there was no relationship found between religious coping methods and well-being and well-being and denominational affiliation

A Comparative Evaluation of Antimicrobial Coated versus Non-antimicrobial Coated Peripherally Inserted Central Catheters on Associated Outcomes: A Randomized Controlled Trial

Background Central line–associated bloodstream infections (CLABSIs) are a common life-threatening risk factor associated with central venous catheters (CVCs). Research has demonstrated benefit in reducing CLABSIs when CVCs coated with antimicrobials are inserted. The impact of chlorhexidine (CHG)-impregnated versus non-CHG peripherally inserted central catheters (PICCs) on risk of CLABSI is unknown. Venous thromboembolism (VTE) is also a complication associated with CVCs. This study compares the impact of both PICC lines on these outcomes. Methods Patients in 3 high-risk units were randomly assigned to receive either a CHG-impregnated or non-CHG PICC line. Laboratory data were collected and reviewed daily on all study patients. The PICC dressing site was assessed daily. Medical record documentation was reviewed to determine presence of CLABSI or VTE. Results There were 167 patients who completed the study. Three patients developed CLABSI (2 in the CHG group, and 1 in the non-CHG group), and 3 patients developed VTE (2 in the non-CHG group, and 1 in the CHG group). No significant relationship was noted between the type of PICC line on development of a CLABSI (P = .61) or VTE (P > .99). A significant difference was noted in moderate bleeding (P ≤ .001) requiring thrombogenic dressing in the patients who had the CHG PICC line. Conclusions No differences were noted in the development of CLABSI and VTE between the CHG and non-CHG groups

Strategies to promote the implementation of Screening, Brief Intervention, and Referral to Treatment (SBIRT) in healthcare settings: a scoping review

Background: Screening, brief intervention, and referral to treatment (SBIRT), is an approach for the prevention and treatment of substance use disorders, but is often underutilized in healthcare settings. Although the implementation of SBIRT is challenging, the use of multi-faceted and higher intensity strategies are more likely to result in the successful incorporation of SBIRT into practice in primary care settings. SBIRT may be used in different healthcare settings, and the context for implementation and types of strategies used to support implementation may vary by setting. The purpose of this scoping review is to provide an overview regarding the use of strategies to support implementation of SBIRT in all healthcare settings and describe the associated outcomes. Methods: A scoping review was conducted using CINAHL Complete, HealthBusiness FullTEXT, PsycINFO, PubMed, and Embase to search for articles published in English prior to September 2019. The search returned 462 citations, with 18 articles included in the review. Two independent reviewers extracted data from each article regarding the theory, design, timeline, location, setting, patient population, substance type, provider, sample size and type, implementation strategies, and implementation outcomes. The reviewers entered all extracted data entered into a table and then summarized the results. Results: Most of the studies were conducted in the United States in primary care or emergency department settings, and the majority of studies focused on SBIRT to address alcohol use in adults. The most commonly used strategies to support implementation included training and educating stakeholders or developing stakeholder interrelationships. In contrast, only a few studies engaged patients or consumers in the implementation process. Efforts to support implementation often resulted in an increase in screening, but the evidence regarding the brief intervention is less clear, and most studies did not assess the reach or adoption of the referral to treatment. Discussion: In addition to summarizing the strategies used to increase reach and adoption of SBIRT in healthcare settings, this scoping review identified multiple gaps in the literature. Two major gaps include implementation of SBIRT in acute care settings and the application of implementation theories to inform healthcare efforts to enable use of SBIRT

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths

A Comparative Evaluation of Antimicrobial Coated versus Non-antimicrobial Coated Peripherally Inserted Central Catheters on Associated Outcomes: A Randomized Controlled Trial

Background Central line–associated bloodstream infections (CLABSIs) are a common life-threatening risk factor associated with central venous catheters (CVCs). Research has demonstrated benefit in reducing CLABSIs when CVCs coated with antimicrobials are inserted. The impact of chlorhexidine (CHG)-impregnated versus non-CHG peripherally inserted central catheters (PICCs) on risk of CLABSI is unknown. Venous thromboembolism (VTE) is also a complication associated with CVCs. This study compares the impact of both PICC lines on these outcomes. Methods Patients in 3 high-risk units were randomly assigned to receive either a CHG-impregnated or non-CHG PICC line. Laboratory data were collected and reviewed daily on all study patients. The PICC dressing site was assessed daily. Medical record documentation was reviewed to determine presence of CLABSI or VTE. Results There were 167 patients who completed the study. Three patients developed CLABSI (2 in the CHG group, and 1 in the non-CHG group), and 3 patients developed VTE (2 in the non-CHG group, and 1 in the CHG group). No significant relationship was noted between the type of PICC line on development of a CLABSI (P = .61) or VTE (P > .99). A significant difference was noted in moderate bleeding (P ≤ .001) requiring thrombogenic dressing in the patients who had the CHG PICC line. Conclusions No differences were noted in the development of CLABSI and VTE between the CHG and non-CHG groups

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths