Looking for a good sample?

Let us find the best one for you! What is your topic?

Over 850,000 documents to help brainstorm your essay topic

Haven't found the Essay You Want?
GET YOUR CUSTOM ESSAY SAMPLE
For Only $13/page
3 views

Research Critique Essays

It is uncertain whether lower levels of staffing by nurses at hospitals are associated with an increased risk that patients will have complications. Therefore, the authors conducted a regression analyses to examine the relationship between the amount of care provided by nurses in hospital’s and the patients’ outcome.  A regression analysis was used to help demonstrate the relationship between a dependent variable and independent variables. “It helps us understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed”(Lindley 1987). With this understanding of what a regression analyses is, researchers conducted their research by obtaining data from 1997 and 1998 on hospital discharges and the staffing by nurses (registered nurses, licensed practical nurses, and nurses’ aides) from 11 states. The purpose of this was to determine the relationship between the amount of care provided by nurses in hospitals and patients’ outcomes.  From this collection of data, the authors determined that among medical patients, a higher proportion of hours of care per day provided by registered nurses were ultimately associated with a shorter length of stay and lower incidence of iatrogenic illnesses. Among surgical patients, a similar outcome applied but due to a significant difference in the sample size of these surgical patients these results were not as accurate.
            Several variables were collected while conducting this research. Population was obtained on hospital discharges and the staffing by nurses from 11 states (Arizona, California, Maryland, Massachusetts, Missouri, Nevada, New York, South Carolina, Virginia, West Virginia, and Wisconsin). Of the 11 states, they were able to narrow down to a sample size of 5,075,969 discharges of medical patients and 1,104,659 of surgical patients. They then estimated 1997 staffing as the weighted average of staffing for the fiscal years of 1997 and 1998. Their initial sample was 1041 hospitals however, 20 hospitals were eliminated due to either missing data on staffing, occupancy rate less than 20%, and as well as those hospitals that reported extremely high or low staffing per patient day. Therefore, the final sample used for analysis was 799 hospitals, which accounted for 26% of the discharges from nonfederal hospitals in the United States in 1997.
Researchers analyzed their data by calculating the length of stay, the rates of adverse outcomes such as urinary tract infections, pressure ulcers, hospital-acquired pneumonia, hospital-acquired-sepsis, shock or cardiac arrest, in-hospital death, failure to rescue, deep vein thrombosis and the hours of nursing care provided per day. Regression analysis was performed with the use of nurse-staffing and control variables. Characteristics of the hospitals factored in were: the location of the hospitals, the number of beds in the medical and surgical unit of hospitals, and lastly, the teaching status of these hospitals.  The least squares regression, a way to estimate the u known parameters, was then used to differentiate between actual and expected length of stay. A negative binomial regression model was also used to determine the ratio of each adverse outcome (incidence ratio). After controlling for other variables, they estimated the differences in outcomes between hospitals with staffing levels of Registered Nurses at the 75th percentile and hospitals with staffing levels of Registered Nurses at the 25th percentile.
            The results researchers have found was that the mean number of hours of nursing care provided by licensed Registered Nurses per-patient day was 7.8±1.9.  “A higher proportion of hours of care from these Registered Nurses showed a shorter length of stay and lower rates of urinary tract infection, hospital-acquired pneumonia, upper gastrointestinal bleeding, sepsis, failure to rescue and in-hospital deaths”(Needleman, Buerhaus, Mattke, Zevelinsky). Similar findings applied to surgical patients as well, where a higher proportion of hours of care showed a shorter length of stay and lower rates of wound infection, pulmonary failure, and metabolic derangement. However, researchers claim “there was a lower rate of adverse outcomes which in part could be due to the fact that surgical patients may be healthier than medical patients and therefore have a lower risk of adverse outcomes”(Needleman, Buerhaus, Mattke, Zevelinsky). Also, the size of the sample taken for surgical patients may have also affected the outcome where only 1,104,659 surgical patients were sampled versus 5,075,969 medical patients. This would have made it more difficult to detect any associations between the two.
            The conclusion of the research proves to be faulty in some areas. Although many variables were taken into account while performing the regression analyses, such as, types of patients, level of care and length of nursing hours, still there are many lurking variables surrounding the relationship between quality of care and nursing hours. Results found indicated that the complications common in hospitalized patients were urinary tract infections, pneumonia, and metabolic derangements. The mean deaths rates were 18.6% among medical patients and 19.7% among the surgical patients. These rates were similar across the 11 states measured.
            This study could be improved by taking the education background of the nurses staffed into consideration and incorporating it into the research. This could be accomplished by collecting data from the same population taken previously. By disregarding this factor from analysis could influence the statistics of this research. This is what would be considered a “lurking variable” that could have caused two extremely different outcomes. It could have either enhanced nursing care, resulting in a reduction of adverse outcomes and nursing hours spent, or provided the same outcome of care however with an increased amount of nursing hours spent. With this kept in mind, it seems the level of staffing by nurses is considered an incomplete measure of the quality of nursing care in hospitals and therefore makes it difficult for researchers to determine the outcome accurately. Another factor that was not taken into consideration was the use of effective communication. Effective communication not only allows the nurse to form a therapeutic nurse-patient relationship, it also allows the nurse to perform assessments thoroughly, and provide care in a timely manner that maximizes patient care preventing any undue adverse outcomes.  The availability of necessary equipment in a facility also plays a crucial factor in improving the quality of patient care. Also, the amount of care provided is also dependent on the severity of each patient’s condition. This seems to be a variable that cannot be measured as each patient is unique to each individual. Research could possibly be narrowed according to each population group, making it more specific, perhaps analyzing patients only with diabetes in the medical units. With the results provided, researchers have been able to show that the more hours provided per patient decreases their length of stay. I feel this research is a great basis for further analysis that would help solidify the correlation between low nurse staffing levels and poor quality of care in hospitals.

Reference:
Lindley, D.V. (1987). Regression and correlation analysis. New Palgrave: A Dictionary of Economics, v. 4, pp. 120-123.
 Needleman, J., Buerhaus, P., Mattke, S., Zevelinsky, K. (2002). Nurse staffing levels and                     the quality of care in hospitals. The New England Journal of Medicine, 346(22),1715-22.
 

Sorry, but copying text is forbidden on this website. If you need this or any other sample register now and get a free access to all papers, carefully proofread and edited by our experts.

Sign Up Login We can't stand spam as much as you do No, thanks. I prefer suffering on my own