| 1. |
- Cummings, Greta G, et al.
(författare)
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Influence of organizational characteristics and context on research utilization
- 2007
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Ingår i: Nursing Research. - 0029-6562 .- 1538-9847. ; 56:4 Suppl, s. 24-39
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Despite three decades of empirical investigation into research utilization and a renewed emphasis on evidence-based medicine and evidence-based practice in the past decade, understanding of factors influencing research uptake in nursing remains limited. There is, however, increased awareness that organizational influences are important.OBJECTIVES: To develop and test a theoretical model of organizational influences that predict research utilization by nurses and to assess the influence of varying degrees of context, based on the Promoting Action on Research Implementation in Health Services (PARIHS) framework, on research utilization and other variables.METHODS: The study sample was drawn from a census of registered nurses working in acute care hospitals in Alberta, Canada, accessed through their professional licensing body (n = 6,526 nurses; 52.8% response rate). Three variables that measured PARIHS dimensions of context (culture, leadership, and evaluation) were used to sort cases into one of four mutually exclusive data sets that reflected less positive to more positive context. Then, a theoretical model of hospital- and unit-level influences on research utilization was developed and tested, using structural equation modeling, and 300 cases were randomly selected from each of the four data sets.RESULTS: Model test results were as follows--low context: chi2= 124.5, df = 80, p <. 001; partially low: chi2= 144.2, p <. 001, df = 80; partially high: chi2= 157.3, df = 80, p <. 001; and partially low: chi2= 146.0, df = 80, p <. 001. Hospital characteristics that positively influenced research utilization by nurses were staff development, opportunity for nurse-to-nurse collaboration, and staffing and support services. Increased emotional exhaustion led to less reported research utilization and higher rates of patient and nurse adverse events. Nurses working in contexts with more positive culture, leadership, and evaluation also reported significantly more research utilization, staff development, and lower rates of patient and staff adverse events than did nurses working in less positive contexts (i.e., those that lacked positive culture, leadership, or evaluation).CONCLUSION: The findings highlight the combined importance of culture, leadership, and evaluation to increase research utilization and improve patient safety. The findings may serve to strengthen the PARIHS framework and to suggest that, although it is not fully developed, the framework is an appropriate guide to implement research into practice.
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| 2. |
- Estabrooks, Carole A, et al.
(författare)
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Predicting research use in nursing organizations : a multilevel analysis
- 2007
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Ingår i: Nursing Research. - 0029-6562 .- 1538-9847. ; 56:4 Suppl, s. 7-23
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: No empirical literature was found that explained how organizational context (operationalized as a composite of leadership, culture, and evaluation) influences research utilization. Similarly, no work was found on the interaction of individuals and contextual factors, or the relative importance or contribution of forces at different organizational levels to either such proposed interactions or, ultimately, to research utilization.OBJECTIVE: To determine independent factors that predict research utilization among nurses, taking into account influences at individual nurse, specialty, and hospital levels.DESIGN: Cross-sectional survey data for 4,421 registered nurses in Alberta, Canada were used in a series of multilevel (three levels) modeling analyses to predict research utilization.METHODS: A multilevel model was developed in MLwiN version 2.0 and used to: (a) estimate simultaneous effects of several predictors and (b) quantify the amount of explained variance in research utilization that could be apportioned to individual, specialty, and hospital levels.FINDINGS: There was significant variation in research utilization (p <.05). Factors (remaining in the final model at statistically significant levels) found to predict more research utilization at the three levels of analysis were as follows. At the individual nurse level (Level 1): time spent on the Internet and lower levels of emotional exhaustion. At the specialty level (Level 2): facilitation, nurse-to-nurse collaboration, a higher context (i.e., of nursing culture, leadership, and evaluation), and perceived ability to control policy. At the hospital level (Level 3): only hospital size was significant in the final model. The total variance in research utilization was 1.04, and the intraclass correlations (the percent contribution by contextual factors) were 4% (variance = 0.04, p <.01) at the hospital level and 8% (variance = 0.09, p <.05) at the specialty level. The contribution attributable to individual factors alone was 87% (variance = 0.91, p <.01).CONCLUSIONS: Variation in research utilization was explained mainly by differences in individual characteristics, with specialty- and organizational-level factors contributing relatively little by comparison. Among hospital-level factors, hospital size was the only significant determinant of research utilization. Although organizational determinants explained less variance in the model, they were still statistically significant when analyzed alone. These findings suggest that investigations into mechanisms that influence research utilization must address influences at multiple levels of the organization. Such investigations will require careful attention to both methodological and interpretative challenges present when dealing with multiple units of analysis.
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| 3. |
- Midodzi, William K, et al.
(författare)
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An alternative approach to addressing missing indicators in parallel datasets : research utilization as a phantom latent variable
- 2007
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Ingår i: Nursing Research. - 0029-6562 .- 1538-9847. ; 56:4 Suppl, s. 47-52
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Tidskriftsartikel (refereegranskat)abstract
- When doing secondary data analysis, it is not uncommon to find that a key variable was not measured. Often the researcher has no option but to do without the missing indicator, but when nearly parallel datasets exist, the researcher may have other options. In an earlier article leading up to this special issue, this research team was confronted with the problem that research utilization had been measured in only one of two similar datasets, namely, in the 1996 but not the 1998 Alberta Registered Nurse survey. The 1998 dataset had a larger sample size (6,526 compared to 600 nurse respondents in 1996) and a stronger set of measured variables, but was missing the key variable of interest--research utilization. To overcome this, a regression-based strategy was used to create a research utilization score for each nurse in the 1998 survey by exploiting the availability of several anticipated causes of research utilization in both datasets. Presented here is an alternative and more complicated procedure that might be applied in future investigations. The article presents a methodological understanding of how to use a phantom variable to account for the unmeasured research utilization variable in a two-group structural equation model. This approach could be used to overcome several of the limitations connected to using a regression-based approach to creating a key missing variable when nearly parallel datasets are available.
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