Social Work Perspectives of Quality in Nursing Homes Compared to Minnesota Nursing Home Report Card and Nursing Home Compare

The number of older adults requiring nursing home level of care continues to rise and is expected to continue as the baby boom generation ages. The quality of nursing home care has been a significant policy issue for several years, as poor quality of care continues to be an endemic problem in many of the U.S nursing homes. The Nursing Home Reform Act passed in 1987 was designed to set quality standards to improve nursing home care quality. In 1998 the Centers for Medicare and Medicaid responded by implementing Nursing Home Compare, which is a tool to inform consumers about nursing home quality. Social workers often play a key role in advocating for resident rights and ensuring residents’ psychosocial needs are being met. Care quality can have a large impact on the overall wellbeing of a resident. The purpose of this study was to learn about nursing home social workers perspectives of what quality care is and if their perspectives are similar to quality indictors identified on the Minnesota Nursing Home Report Card and Nursing Home Compare. Eight nursing home social workers participated in individual semi-structured interviews answering fourteen questions regarding their perspectives of quality care in the nursing home setting. The participants’ responses demonstrated similar quality care indicators compared to the Minnesota Nursing Home Report Card and Nursing Home Compare. Participants’ responses evolved into themes regarding resident centered care and quality of life, quality indicators and lack of response, staffing ratios, retention and burnout, leadership and empowerment, awareness and use of report cards and informed consumers. Developing an understanding of indicators that contribute towards good quality care in the nursing home setting will allow social workers to advocate for residents to ensure they experience the highest achievable quality of life possible

Social Work Perspectives of Quality in Nursing Homes Compared to Minnesota Nursing Home Report Card and Nursing Home Compare

The number of older adults requiring nursing home level of care continues to rise and is expected to continue as the baby boom generation ages. The quality of nursing home care has been a significant policy issue for several years, as poor quality of care continues to be an endemic problem in many of the U.S nursing homes. The Nursing Home Reform Act passed in 1987 was designed to set quality standards to improve nursing home care quality. In 1998 the Centers for Medicare and Medicaid responded by implementing Nursing Home Compare, which is a tool to inform consumers about nursing home quality. Social workers often play a key role in advocating for resident rights and ensuring residents’ psychosocial needs are being met. Care quality can have a large impact on the overall wellbeing of a resident. The purpose of this study was to learn about nursing home social workers perspectives of what quality care is and if their perspectives are similar to quality indictors identified on the Minnesota Nursing Home Report Card and Nursing Home Compare. Eight nursing home social workers participated in individual semi-structured interviews answering fourteen questions regarding their perspectives of quality care in the nursing home setting. The participants’ responses demonstrated similar quality care indicators compared to the Minnesota Nursing Home Report Card and Nursing Home Compare. Participants’ responses evolved into themes regarding resident centered care and quality of life, quality indicators and lack of response, staffing ratios, retention and burnout, leadership and empowerment, awareness and use of report cards and informed consumers. Developing an understanding of indicators that contribute towards good quality care in the nursing home setting will allow social workers to advocate for residents to ensure they experience the highest achievable quality of life possible

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

High-resolution population estimation using household survey data and building footprints.

The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses

High-resolution population estimation using household survey data and building footprints

The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.</p