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Citation: Dunton, N., Gajewski, B., Klaus, S., Pierson, B., (September 30, ) " The Relationship of Nursing Workforce Characteristics to Patient Outcomes". The Relationship of Glycemic Exposure (HbA1c) to the Risk of Development and Progression of Retinopathy in the Diabetes Control and Complications Trial. relationship meaning, definition, what is relationship: the way in which two people or two group: Learn more.
Now what I challenge you to do is pause this video, and using just the information here, and you don't need a calculator; in fact, you can't use a calculator. I forbid you; try to figure out what a, b, c, and d are just using your powers of reasoning, no calculator involved. Just use your powers of reasoning. Can you figure out what a, b, c, and d are?
I'm assuming you've given a go at it, so let's see what we can deduce from this. Here, we have just a bunch of numbers. We need to figure out what b is. These are all b to the 1.
I don't really know what to make sense of this stuff here.
Relationship | Definition of Relationship by Merriam-Webster
Maybe this table will help us. Let me do these in different colors. This first column right over here tells us that log base b of a, so now y is equal to a, that that is equal to 0. Now this is an equivalent statement to saying that b to the a power is equal to This is an equivalent statement to saying b to the 0 power is equal to a.
This is saying what exponent do I need to raise b to to get a? You raise it to the 0 power. This is saying b to the 0 power is equal to a. Now what is anything to the 0 power, assuming that it's not 0? If we're assuming that b is not 0, if we're assuming that b is not 0, so we're going to assume that, and we can assume, and I think that's a safe assumption because where we're raising b to all of these other powers, we're getting a non-0 value.
Since we know that b is not 0, anything with a 0 power is going to be 1.
This tells us that a is equal to 1. We got one figured out. Now let's look at this next piece of information right over here. What does that tell us? That tells us that log base b of 2 is equal to 1. This is equivalent to saying the power that I needed to raise b to get to to 2 is 1. Or if I want to write in exponential form, I could write this as saying that b to the first power is equal to 2. I'm raising something to the first power and I'm getting 2?
What is this thing? That means that b must be 2. So b is equal to 2. You could say b to the first is equal to 2 to the first. That's also equal to 2.
- Relationship between exponentials & logarithms: tables
So b must be equal to 2. Review of Previous Nursing Outcomes Studies This section will present the growing body of evidence that describes the relationship between hospital nursing workforce attributes, such as nurse staffing levels, and patient outcomes.
Because many of these studies have had significant limitations in conceptual framework, design, and nursing workforce attributes, this section will also discuss the limitations of these studies. The AHRQ review identified 97 observational studies published between and and included 94 of these reports in a meta-analysis.
This meta-analysis found strong and consistent evidence that higher registered nurse RN hours were related to lower patient mortality rates, lower rates of failure to rescue, and lower rates of hospital-acquired pneumonia. There was evidence that higher, direct care RN hours was related to shorter lengths of stay.
Higher total nursing hours also were found to result in lower hospital mortality and failure to rescue rates, and in shorter lengths of stay. Based on fewer studies, the review found evidence that the prevalence of baccalaureate-prepared RNs was related to lower hospital mortality rates, that higher RN job satisfaction and satisfaction with workplace autonomy were related to lower hospital mortality rates, and that higher rates of nurse turnover were related to higher rates of patient falls.
The conclusion of the meta-analysis was that higher nurse staffing was associated with better patient outcomes, but that the association was not necessarily causal. Further, the associations varied by service line and unit type. A recent study by Needleman, Buerhaus, Stewart, Zelevinsky, and Soeren demonstrated the business case, i.
The cost of increasing RN's proportion of nursing hours was less than the cost that would have resulted from adverse events, such as failure to rescue, urinary tract infections, hospital-acquired pneumonia, upper gastrointestinal bleeding, shock, and cardiac arrest.
More than 90 percent of the cost savings was associated with reduced length of stay. Limitations of Previous Studies Significant gaps remain in nursing outcomes research literature. These gaps need to be addressed to strengthen the case for including nursing quality indicators in public reporting and value-based purchasing initiatives and to provide guidance to nurse executives regarding staffing models.
Work is needed in the specification of theoretical or conceptual models, including the analysis of unit-level, rather than hospital-level, data. A number of authors have also noted the need to examine additional work-related, structure measures. Finally appropriate data sets for the analysis are also needed. These limitations are addressed in the following sections. Nursing outcomes research typically is based on Donabedian'sconceptual framework, or derivations thereof, in which the structure of care influences the processes of care, and both in turn influence the outcomes of care.
Because this framework supports many variations in actual model specification, many different organizational characteristics have been investigated. For example, different nursing workforce characteristics have been used as measures of the structure of hospital care; and the outcomes of a variety of different health conditions have been studied.
The Donabedian framework implies a hierarchical analysis model, in which patients are embedded in hospital units that have both structural characteristics and processes, and units are embedded within hospitals that have both structural characteristics and processes.
Only a few studies, particularly studies published since the s, had access to datasets that supported a hierarchical analysis. Failure to use a hierarchical model of analysis results in mis-estimation of the relationship between nursing workforce characteristics and patient outcomes.
The Relationship of Nursing Workforce Characteristics to Patient Outcomes
It is important to note that some valuable studies have used the hospital service line e. In a different approach Whitman, Kim, Davidson, Wolf, and Wang have argued for the patient care unit, including unit specialty, as the unit of analysis because it is the operational level with the responsibility for care. A few authors have actually used the patient care unit as the unit of analysis e. Studies with data for service lines or unit types have demonstrated that specific aspects of the nursing workforce may be significant for some service lines or unit patient outcomes and not for others e.
Nursing workforce characteristic limitations. Although most previous research on the relationship between nursing workforce characteristics and patient outcomes has used nursing hours or patient-to-nurse ratios, a few studies have examined other characteristics, such as education, job satisfaction, or turnover.
Additional measures of characteristics of the nursing workforce, such as measures of nursing processes, are needed, as are improvements in data quality, including larger sample size, reduced bias, and reduced measurement error. However, the nursing workforce should simultaneously be characterized in terms of supply hours ; knowledge, expertise, and experience; job satisfaction; and fitness fatigue.
Theoretically based measures of nursing processes, such as assessment, surveillance and monitoring, nursing interventions, communication with other health care providers, and patient education, should also be included in analyses. The data available for nursing outcomes research have generally come from three types of sources. First, analysts have used large national data sets, such as hospital discharge abstracts or Medicaid costs reports, and matched those with nurse staffing data from selected states.
Generally, such data sets are limited to information for the largest states and do not have data at the unit level. As a consequence, measures of the nursing workforce cannot distinguish between nurses in direct patient care or those involved in administrative or outpatient activities. While these data sets have information on a large number of patient outcomes, the nursing workforce indicators are quite limited.
Second, analysts have obtained data from individual states or subsets of hospital surveys, administrative data, or hospital primary data collections. The California Nursing Outcomes Coalition Database and the Veterans Administration Nursing Outcomes Database are good examples of datasets that have unit-level information on both a variety of nursing workforce characteristic and patient outcomes for a subset of the nation's hospitals.
Third, some analysts have collected data from convenience samples of a small number of hospitals to which they have access. It is questionable whether findings from these convenience-sample studies can be generalized to larger populations. Finally, most studies are based on cross-sectional data sets.