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Biomedical subjects

Susan M Fox-Wasylyshyn

Publications and source records attributed to Susan M Fox-Wasylyshyn.

8 recordsLinked to original sources
PubMed2006

Nurses' roles with families: perceptions of ICU nurses.

This descriptive survey examined: (a) differences between nurses' (N=47) perceptions of self-performance and that of their colleagues with regard to their roles with family members of intensive care patients, and (b) the impact of nurses' comfort on their role enactment as it relates to family focused interventions. Participants rated their self-performance higher than that of their colleagues with respect to 15 of the 19 items, suggesting that they think they perform better than their colleagues. The results also showed that nurses' comfort was positively correlated with their role enactment as it pertains to discussing patient prognosis (r=.496; p<.001), explaining patient's equipment (r=.43; p=.003), and discussing the possibility of death with family members (r=.43; p=.003).

Attitude of Health Personnel↗
PubMed2005

Handling missing data in self-report measures.

Self-report measures are extensively used in nursing research. Data derived from such reports can be compromised by the problem of missing data. To help ensure accurate parameter estimates and valid research results, the problem of missing data needs to be appropriately addressed. However, a review of nursing research literature revealed that issues such as the extent and pattern of missingness, and the approach used to handle missing data are seldom reported. The purpose of this article is to provide researchers with a conceptual overview of the issues associated with missing data, procedures used in determining the pattern of missingness, and techniques for handling missing data. The article also highlights the advantages and disadvantages of these techniques, and makes distinctions between data that are missing at the item versus variable levels. Missing data handling techniques addressed in this article include deletion approaches, mean substitution, regression-based imputation, hot-deck imputation, multiple imputation, and maximum likelihood imputation.

Data Collection↗
PubMed2005

Enhancing health-care research: an interdisciplinary collaborative approach.

Many research programs tackle complex problems that cannot be comprehensively investigated by a sole researcher or a research team from a single profession. Interdisciplinary teams can develop a collective mass of common knowledge, broaden the scope of research, and produce more clinically relevant outcomes that are sensitive to the realities of practice. The authors describe the experience of a research team from the perspective of its members. The purposes of the paper are to highlight the benefits of an interdisciplinary collaborative approach to research and to describe the characteristics of a successful team. Some of the benefits discussed include increased research productivity and quality, professional development and mentorship, support and encouragement, expanded resource networks, and bridging of the gap between academia and practice. The authors also discuss the characteristics of a successful research team, associated challenges, and recommendations for enhancing research endeavours through collaboration.

Health Services Research↗
PubMed2005

Missing data: an introductory conceptual overview for the novice researcher.

Missing data is a common issue in research that, if improperly handled, can lead to inaccurate conclusions about populations. A variety of statistical techniques are available to treat missing data. Some of these are simple while others are conceptually and mathematically complex. The purpose of this paper is to provide the novice researcher with an introductory conceptual overview of the issue of missing data. The authors discuss patterns of missing data, common missing-data handling techniques, and issues associated with missing data. Techniques discussed include listwise deletion, pairwise deletion, case mean substitution, sample mean substitution, group mean substitution, regression imputation, and estimation maximization.

Algorithms↗
PubMedDate not supplied

Family perceptions of nurses' roles toward family members of critically ill patients: a descriptive study.

OBJECTIVES: The purpose of this study was to examine the role of the nurse with families of critically ill patients as perceived by family members and whether meeting family expectations impact family satisfaction. METHODS: A descriptive design was conducted with 29 family members from a 19-bed intensive care unit. Nonparametric statistics, known for their appropriateness for small samples, were used to examine the research questions. RESULTS: Overall, the expectations that family members held regarding nurses' roles with families were not different from their perceptions of what nurses actually did (P = .087). Family members were more satisfied with care when nurses' performance either met or exceeded their expectations (P = .046). CONCLUSION: Nurses did well with regard to meeting family members' expectations. Variations in expectations among family members reflect their diversity and highlight the importance of assessing family needs on a case-by-case basis.

Adult↗
PubMedDate not supplied

Predicting nosocomial bloodstream infections using surrogate markers of injury severity: clinical and methodological perspectives.

BACKGROUND: Injury severity indices are numerical scores that are utilized to predict nosocomial bloodstream infections (BSI) in critically ill patients. However, surrogate markers of injury severity (SMIS) may be more clinically meaningful than these commonly used numerical injury severity indices with respect to the control and prevention of nosocomial BSI. OBJECTIVE: The purpose of this study was to demonstrate the clinical and research implications of using the SMIS in predicting nosocomial BSI. METHOD: A prospective nonexperimental cohort study was conducted on 361 critically ill trauma patients. Three logistic regression models were examined for their clinical relevance and statistical parsimony. The first model included the Injury Severity Score (ISS) and 5 other independent predictors, and excluded the SMIS. The second model included all study variables. The third model excluded the ISS. RESULTS: The analysis suggested that number of blood units transfused, number of central venous catheters inserted, and use of chest tube(s) were the SMIS. The ISS was found to be an independent predictor of nosocomial BSI only when the SMIS were not included in the model. The model that included the SMIS and excluded the ISS explained the highest variance in nosocomial BSI and had the best negative predictive value (93%). DISCUSSION: Clinicians can use knowledge of SMIS to develop interventions that minimize the risk of nosocomial BSI. Hence, the SMIS can serve not only as a prediction tool but also as a way to enhance control and prevention strategies for BSI.

Analysis of Variance↗
PubMedDate not supplied

Severe acute respiratory syndrome: another challenge for critical care nurses.

Severe acute respiratory syndrome (SARS) is a viral disease that may be contracted by exposure to a newly recognized form of the coronavirus. It often manifests through a set of common respiratory symptoms that include fever and nonproductive cough. To date, SARS has no vaccine or definitive treatment. Approximately 20% of SARS patients develop respiratory failure, which requires mechanical ventilation and close cardiopulmonary monitoring. Intensive care unit (ICU) nurses and other healthcare workers who care for SARS patients are at risk of contracting the disease. Thus, it is important that ICU nurses be familiar with the disease and its implications for critical care. This article provides critical care nurses with an update on the first SARS outbreak, its origin, case definition, clinical manifestations, diagnosis, relevant infection control practices, management, and recommendations for the role of ICU nurses in dealing with future outbreaks.

Communicable Diseases, Emerging↗