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PURPOSE: The purpose of this study was to examine knowledge, attitude, and barriers toward care planning documentation practices with standardized nursing languages (SNLs) of nurses and nursing students at a midwestern healthcare system, comparing student and nurse responses. METHODS: Cross-sectional surveys were given over a 2-month period with nurses and nursing students at different sites in a midwestern healthcare system, using convenience sampling. The Knowledge, Attitude, and Barriers to Using Standardized Nursing Languages and Current Practices Survey was adapted for use and re-tested for validity/reliability (Content Validity Index 0.81-1.00; Cronbach alpha = 0.82-0.99) with 28 Likert scale items measuring knowledge, attitude, and barriers. Descriptive statistics, composite scores, correlations, t-tests, and multiple regression were used to analyze the concepts of the tool. FINDINGS: 134/400 RNs responded (34%); 109/116 students responded (93.9%). Data analyses indicate adequate to superior levels of knowledge related to SNLs and NANDA International, Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC), collectively referred to as NNN (NANDA, NIC, & NOC), positive attitudes toward SNLs/NNN and for adopting SNLs/NNN into documentation practices, but moderate to great barriers for implementation in practice. Barriers included lack of financial resources for change, lack of mentors, and lack of mandates to use SNLs. Students scored significantly higher than nurses in attitude only. CONCLUSIONS: Perceptions of nurses and student nurses for current documentation indicate awareness of inadequacy in existing systems and willingness to change existing systems for standardized languages, with perceived barriers to change/implementation of SNLs. Students were more positive about SNLs than nurses. IMPLICATIONS FOR NURSING PRACTICE: Major implications for nursing are to reevaluate electronic documentation systems and determine how to insert and easily apply SNLs in these systems, such that nursing care documentation is standardized, interoperable, effective, time-saving, and attainable.

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Providing 80% of healthcare worldwide, nurses focus on physiologic and psychosocial aspects of health, which incorporate social determinants of health (SDOH). Recognizing their important role in SDOH, nurse informatics scholars included standardized measurable terms that identify and treat issues with SDOH in their classification systems, which have been readily available for over 5 decades. In this Perspective, we assert these currently underutilized nursing classifications would add value to health outcomes and healthcare, and to the goal of decreasing disparities. To illustrate this, we mapped 3 rigorously developed and linked classifications: NANDA International (NANDA-I), Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC) called NNN (NANDA-I, NIC, NOC), to 5 Healthy People 2030 SDOH domains/objectives, revealing the comprehensiveness, usefulness, and value of these classifications. We found that all domains/objectives were addressed and NNN terms often mapped to multiple domains/objectives. Since SDOH, corresponding interventions and measurable outcomes are easily found in standardized nursing classifications (SNCs), more incorporation of SNCs into electronic health records should be occurring, and projects addressing SDOHs should integrate SNCs like NNN into their ongoing work.

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PURPOSE: To provide guidance to nurses caring for families with COVID-19, we developed linkages using interoperable standardized nursing terminologies: NANDA International (NANDA-I) nursing diagnoses, Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC). In addition, we wanted to identify gaps in the terminologies and potential new nursing diagnoses, outcomes, and interventions for future development related to nurse roles in family care during a pandemic. METHODS: Using a consensus process, seven nurse experts created the linkages focused on families during the COVID-19 pandemic using the following steps: (1) creating an initial list of potential nursing diagnoses, (2) selecting and categorizing outcomes that aligned with all components of each nursing diagnosis selected, and (3) identifying relevant nursing interventions. FINDINGS: We identified a total of seven NANDA-I nursing diagnoses as the basis for the linkage work. These are distributed in three NANDA-I Domains and based in the psychosocial dimension of the Nursing Care in Response to Pandemics model. Eighty-nine different NOC outcomes were identified to guide care based on the nursing diagnoses, and 54 different NIC interventions were suggested as possible interventions. Fifteen new proposed concepts were identified for future development across the three classifications. CONCLUSIONS: The linkages of nursing diagnoses, outcomes, and interventions provide a guide to enhance nursing practice and care documentation that could quantify the impact of nursing care to patient outcomes for families at risk for or infected by COVID-19. IMPLICATIONS FOR NURSING PRACTICE: NANDA-I, NOC, and NIC linkages identified in this paper provide resources to support clinical decisions and guide critical thinking for nurses encountering care needs of families with COVID-19. Documentation of these linkages provides data that can create new knowledge to enhance the care of families impacted by COVID-19.

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PURPOSE: To provide guidance to nurses caring for individuals with COVID-19, we developed linkages using interoperable standardized nursing terminologies: NANDA International (NANDA-I) nursing diagnoses, Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC). We also identified potential new NANDA-I nursing diagnoses, NOC outcomes, and NIC interventions for future development related to nurses' role during a pandemic. METHODS: Using a consensus process, seven nurse experts created the linkages for individuals during the COVID 19 pandemic using the following steps: (a) creating an initial list of potential nursing diagnoses, (b) selecting and categorizing outcomes that aligned with all components of each nursing diagnosis selected, and (c) identifying relevant nursing interventions. FINDINGS: A total of 16 NANDA-I nursing diagnoses were identified as the foundation for the linkage work, organized in two dimensions, physiological and psychosocial. A total of 171 different NOC outcomes were identified to guide care based on the nursing diagnoses and 96 NIC interventions were identified as suggested interventions. A total of 13 proposed concepts were identified for potential future development across the three classifications. CONCLUSIONS: The linkages of nursing diagnoses, outcomes, and interventions developed in this article provide a guide to enhance nursing practice and determine the effectiveness of nurses' contribution to patient outcomes for individuals at risk for or infected by COVID-19. IMPLICATIONS FOR NURSING PRACTICE: NANDA-I, NOC, and NIC linkages identified in this paper are an important example of the value of using standardized nursing terminologies to guide and document nursing care. When included in electronic health record databases and used widely, the data generated from the care plans can be used to create new knowledge about how to better improve outcomes for patients with COVID-19.

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PURPOSE: We developed linkages using interoperable standardized nursing terminologies, NANDA International (NANDA-I) nursing diagnoses, Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC), to present initial guidance for the development of care plans focused on COVID-19 for nurses practicing in community or public health roles. METHODS: Seven nurse experts identified the linkages of NANDA-I, NOC and NIC for our work related to the COVID-19 pandemic. A model was developed to guide the project. The first step in creating linkages focused on the identification of nursing diagnoses. Then, for each nursing diagnosis, outcomes aligned with all components of the diagnosis were categorized and a list of nursing interventions was selected. The experts used their clinical judgment to make final decisions on the linkages selected in this study. FINDINGS: Two community level nursing diagnoses were identified as key problems appropriate for a pandemic related to COVID-19: Deficient Community Health and Ineffective Community Coping. For the nursing diagnosis Deficient Community Health, eight nursing outcomes and 12 nursing interventions were selected. In comparison for the nursing diagnosis, Ineffective Community Coping, nine nursing outcomes and 18 nursing interventions were identified. A total of40 concepts were identified for future development across the three classifications. CONCLUSIONS: The nursing diagnoses, outcomes and interventions selected during this linkage process provide knowledge to support the community challenged with responding to the COVID-19 pandemic, provide the opportunity to quantify the impact of nursing care, and enhance nursing practice by promoting the use of three standardized terminologies. IMPLICATIONS FOR NURSING PRACTICE: NANDA-I, NOC and NIC linkages identified in this manuscript provide resources to support clinical decisions and care plan development for nurses practicing in the community.

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AIM: This paper is a report of a study comparing an innovative nonlinear model and a traditional linear model for accuracy in prediction of nursing turnover. BACKGROUND: An international, sustained nursing shortage creates a need to target accurately the staff population at risk for turnover. Existing linear methodology is cumbersome with the number of variables needed, while producing inadequate results. Nonlinear modelling methods offer increased simplicity and accuracy in predictability. METHODS: A correlational survey with a longitudinal cohort prospective study was carried out in 2005-2006 with a convenience sample of 1033 Registered Nurses from the Midwest region of the United States of America. At time 1, 756 usable questionnaires were returned and 496 at time 2. Data analysis included analyses of a cusp catastrophe model, a cube-shaped four-dimensional figure with a top that provided a down-turning slope area (the catastrophe/cusp zone). This fluid, dynamic cusp version employed the smallest number of control and dependent variables. RESULTS: The exceedingly small turnover sample preempted the use of the computerized program Cuspfit; a proven quasi-quantitative methodology demonstrated 80.4% predictability in the cusp catastrophe model overall and 53.6% correct predictions of actual terminations, particularly in nurses with <5 years of nursing experience. Additional accurate predictions were obtained with the use of a time-staged model. Organizational commitment and anticipated turnover were accurate predictor variables; job tension was not. CONCLUSION: Catastrophe models are useful in predicting nursing turnover. Future nursing researchers should act on this evidence to benefit forthcoming studies and the profession.

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Recent research suggests that community-based collaboration may build social capital-defined as trust, norms of reciprocity, and networks. Social capital may improve a group's ability to collaborate, manage risk, innovate, and adapt to change. We used mail surveys of group participants and key informant interviews to assess whether the following collaborative group characteristics affected social capital built within 10 collaborative groups in northwest Colorado: perceived success, conflict, activeness, stakeholder diversity, previous collaboration experience, similar values and beliefs, group size, group age, and initial social capital. Perceived success and initial levels of social capital were the strongest predictors of current levels of and changes in social capital over time. Collaboration experience negatively influenced current levels of trust. Our results suggest that collaborative groups may need to consider the outcomes of collaborative interactions in order to build social capital.

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AIM: This paper is a report of a literature review to (1) demonstrate the predictability of organizational commitment as a variable, (2) compare organizational commitment and job satisfaction as predictor variables and (3) determine the usefulness of organizational commitment in nursing turnover research. BACKGROUND: Organizational commitment is not routinely selected as a predictor variable in nursing studies, although the evidence suggests that it is a reliable predictor. Findings from turnover studies can help determine the previous performance of organizational commitment, and be compared to those of studies using the more conventional variable of job satisfaction. METHODS: Published research studies in English were accessed for the period 1960-2006 using the CINAHL, EBSCOHealthsource Nursing, ERIC, PROQUEST, Journals@OVID, PubMed, PsychINFO, Health and Psychosocial Instruments (HAPI) and COCHRANE library databases and Business Source Premier. The search terms included nursing turnover, organizational commitment or job satisfaction. Only studies reporting mean comparisons, R(2) or beta values related to organizational commitment and turnover or turnover antecedents were included in the review. RESULTS: There were 25 studies in the final data set, with a subset of 23 studies generated to compare the variables of organizational commitment and job satisfaction. Results indicated robust indirect predictability of organizational commitment overall, with greater predictability by organizational commitment vs job satisfaction. CONCLUSION: Organizational commitment is a useful predictor of turnover in nursing research, and effective as a variable with the most direct impact on antecedents of turnover such as intent to stay. The organizational commitment variable should be routinely employed in nursing turnover research studies.

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Lipodystrophy severity among 77 people living with HIV/AIDS (PHA) with body fat redistribution was not related to antiretroviral adherence including doses missed during the previous month, categorical rating of maximal adherence, and the PMAQ7 adherence behavior scale. Two thirds of the sample reported submaximal adherence, 19% missing more than two doses, but adherence behavior ratings reflected good overall adherence. Overall symptom burden, convenience of regimen schedule and remembering to organize and take antiretroviral doses, but not regimen adaptation or treatment support, were associated with adherence. Remembering was most strongly related to adherence indicators, retaining statistical significance in adjusted multivariate regression analyses.

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OBJECTIVES: To investigate the relationship between lipodystrophy-specific symptom severity and wellbeing. METHODS: HIV-positive adult patients with body fat redistribution (lipodystrophy syndrome) associated with antiretroviral therapy reported their total non-lipodystrophy symptoms and side effects and completed measures assessing body fat changes (yielding Atrophy, Hypertrophy and Total Lipodystrophy scores), mental health and quality of life. Effects of total symptom complex and lipodystrophy severity on quality of life and mental health were analyzed using Spearman's rho correlations. Logistic regression analyses were utilized to determine the relative-odds of depression produced by overall symptom count and lipodystrophy score increments. RESULTS: Mean ratings for Hypertrophy and Atrophy corresponded to 'very mild' and 'mild' degrees of severity, respectively. The total symptom complex was associated with ratings for most of the mental health and quality of life measures. Patient-perceived body image scores were the sole study variable responsive to lipodystrophy severity ratings. In comparison to reference norms, a pronounced degree of body image impairment was evident. CONCLUSION: Although responsive to the total symptom profile, psychosocial measures typically utilized for evaluating quality of life and mental health status in HIV disease lacked sensitivity and specificity for measuring the consequences of lipodystrophy-associated fat distribution changes alone. Lipodystrophy severity did impact negatively on body image.

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A computer information system, which can provide cost-saving measures useful in adequately allocating nursing staff in the face of decreasing patient stays, increasing patient admissions and discharges, and the unexpected transfers surrounding busy nursing units, is explored.

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There has been a slow, steady exodus of nurses from the acute care setting. However, magnet hospital status is closely correlated with increased retention of nursing staff. The author outlines an assessment plan for nursing staff to determine if magnet status application efforts may be successful for an organization.

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Creating a culture of retention is one antidote to high costs of nurse turnover. However, nurse turnover behavior has proven to be largely uncertain and unpredictable. A new approach to analyzing nurse turnover attitudes and behavior is discussed. A cusp catastrophe nonlinear model of nurse turnover is presented as having usefulness for the prediction of turnover for managerial decision making in nursing care delivery systems. Viewing nursing from the perspective of nonlinear dynamics can create new strategies for effective and efficient nursing services.

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