Problem Statement
All human beings can commit errors; however, errors committed by nurses during their practice are on the rise. Whenever people are tired, their concentration level decreases considerably, hence, there is a very high probability of them committing errors. In most cases, nurses are forced to work for extra hours to compensate for the shortage of the number of registered nurses in hospitals. Obviously, nurses will work with sober minds during the initial hours, but as they become tired, they serve patients inaccurately (Scott, Hofmeister, Rogness, & Rogers, 2010). Essentially, working for more than twelve hours is too much, but nurses are forced to do it because they lack a protocol to air their complaints. Worse of it all is the fact that nurses who work in the intensive care units, surgery units, emergency units, or other specialized units have to avail themselves whenever they are called. Whether nurses receive extra payments for their extra working hours or not, the errors they commit endangers the lives of the patients.
Background
According to previous researches, the extended work shifts are suspected to be some of the major causes of the errors that nurses commit during their practices. In spite of the fact that the assumption is not yet proved, it is evident that most public hospitals have a shortage in the number of registered nurses. Obviously, the present nurses have to work for extra hours to serve the increasing number of patients (Flynn, Liang, Dickson, Xie, & Suh, 2012). Essentially, the government has done very little to combat the issue, and nurses have to work for prolonged shifts with no break or time to recover (Trinkoff, Geiger-Brown, Brady, Lipscomb, & Muntaner, 2010). Moreover, the federal regulations do not have strict regulations on the number of hours that nurses or other health care workers have to work; therefore, the patients bear the consequences, as their safety is not guaranteed.
Hypothesis
- Null hypothesis: Nurses whose shifts extend for more than twelve hours do not commit medication errors.
- Alternative hypothesis: Nurses whose shifts run for twelve hours commit medication errors.
Variable Measurement
In this article, the author measured three variables: (1) shift duration, (2) overtime duration, and (3) total work hours in a week. Therefore, the dependent variable is the number of errors and near error instances. The independent variables comprise of the work duration, number of hours worked per week, and the duration of the overtime. The control variables included the age of the nurses, the type of hospital unit, and the size of the hospital.
Research Design
The researcher used a quantitative research design, as numerical and statistical analyses were necessary in analyzing the relationship between the dependent and independent variables. The quantitative design obliged the researcher to collect first hand information that would help in developing a decisive conclusion.
Sampling: The researcher employed a random sampling technique. Cover letters that explained about the study were mailed to randomly selected members of the American Nurses Association (ANA). Of the 4,320 cover letters that were sent, 1,725 nurses responded to the letters to show their expression of interest. However, only 891 qualified to take part in the study and two logbooks were sent to each one of them. Only 362 respondents were able to fill and mail back the two logbooks while 31 respondents managed to fill one of the logbooks. Therefore, the random sample of respondents totaled to 393 registered nurses across the area of study.
Instrumentation: To alert the nurses of the study, a cover letter that gave details of the study was used. A demographic questionnaire was used to give a clear outlay of the births, deaths, and disease statistics across the health institutions of interest. Finally, two logbooks for a two-week period were used as the key data collection instruments for the study. To ensure that the logbooks were efficient data collection instruments, a pilot test was done to examine the suitability of the data collection items that were contained in the logbooks.
Data Collection: The data collected gave information about the number of hours worked, the duration of overtime, and the sleep/wake patterns. The respondents were supposed to respond to at least forty items daily for all the days that they were on duty. Each nurse recorded the nature of the errors or near-errors committed during each shift in the logbooks. Moreover, nurses were supposed to indicate whether they consumed any stimulants like caffeine during their day offs to facilitate their moods.
Data Analysis: The researcher employed a descriptive statistics approach to make a clear scrutiny of the numeric data contained in the duly filled data collection instruments. Frequency tables were very useful in giving a clear outlay of the demographic data from various hospitals. The authors also computed the weekly work hours that each nurse spent in bedside care. Shift durations were estimated to be 8.5 hours and 12.5 hours to cater for the half-hour transition period. A respondent was said to have worked on overtime whenever the actual work hours surpassed the scheduled hours. Using regression, the authors measured the correlation between adverse events and shift duration. Stratifying shifts were used to measure the effect of the duration of overtime on the possibility of committing an error. At a 0.05 significance level, multivariate analysis measured the association between the dependent and the independent variables, whereas Generalized Estimating Equations (GEEs) determined the odds ratio (OR). The authors used logistic regression models to estimate the likelihood of adverse events occurring when shifts are longer than eight hours.
Authors’ Conclusions
The authors found that more than 50% of the respondents worked for more than 8 hours daily. They also found that 16% of nurses’ shifts extend for more than sixteen hours. Interestingly, one of the respondents reported to have worked for almost twenty-four hours in a particular day. The research analysis indicated that indeed, long working hours had some significant effects on the reported medical errors. Moreover, nurses who worked for prolonged overtimes, and those who worked for more than 40 hours per week were more likely to commit errors than those who worked within the required time length. Essentially, the probability of making an error increased with an increase in the number of working hours. Nurses who worked on overtime were three times more likely to commit errors than nurses who worked within their scheduled work periods (Stimpfel, Sloane, & Aiken, 2012). Based on these findings, the null hypothesis was rejected, which validated the alternative hypothesis.
Research Critique
Based on the study’s results, the researchers made the conclusion that there was a correlation between the independent and dependent variables. The researchers enhanced the internal validity study by selecting the respondents randomly. Moreover, cover letters, demographic questionnaires, and logbooks inquired for the same information from all the respondents. The researchers were able to establish the cause and effect, whereby the extended working hours were found to be the main causes of committing errors in the nursing exercise. However, there are some threats that could have affected the internal and external validity of the research.
Possible Threats to Internal Validity
History: This is an event or chain of events that are likely to occur during the study to influence the responses of the respondents. In a hospital setup, many possible happenings can influence the nurses’ responses. An inciting article stressing on the fact that indeed nurses are oppressed in their nursing role may emerge during the study period. Obviously, such an article would provoke nurses and influence the manner in which they would respond to the questions.
Maturation: Maturation describes the effect that time could have on the responses obtained. In this case, the main instrument of data collection comprised of logbooks that covered a two-week period. The participants were required to record their responses on more than forty items during their shifts. Certainly, filling forty items per day might have been exhausting for the respondents. It is evident that the nurses are overwhelmed with their tightened schedules. Therefore, obliging them to respond to forty items on a daily basis is too much work. This could be one of the reasons that the researcher was only able to obtain 362 duly filled logbooks out of the 891 logbooks that were mailed. In fact, 31 respondents were only able to fill one of the logbooks, which is a clear indication that the logbook was too involving. Probably those who filled the two logbooks did it carelessly as the daily trend of filling the questionnaire was depressing.
Testing: As indicated, a pilot test was done, and to some extent, the pilot test can influence the responses of the respondents. In cases where pilot tests are done repeatedly, the respondents may answer the questions fluently in the real study because they are familiar with the data collection instrument. Certainly, the number of times a respondent is tested changes the effect of the variable to be measured. In fact, the pretest makes the respondents to be aware of what is expected of them, and they may formulate ready answers before the real study, where, their answers would depend on their attitudes.
Instrumentation: The research report does not indicate whether the pilot test caused the researchers to make some changes in the data collection instruments of the final study. However, if indeed the post-test instruments contained different instructions, the responses would be affected.
Regression artifact: The study sample was considerably smaller. Therefore, the score of one respondent would have an effect on the average score. Moreover, if all the respondents took part in the pilot study, their scores in the final study were likely to change. The individuals who scored extremely highly and those who scored lowly during the pilot study were likely to score moderately during the real study.
Selection bias: To some extent, the researcher’s choice of respondents is questionable. Preexisting differences between the researcher and the respondents would bring in some bias in the selection of the eligible candidates. In this case, 1725 respondents showed some interest in the study, but the researcher chose 891 and disqualified the rest.
Experimental mortality: This incident occurs when some participants drop out of the study. In this case, the number of respondents dropped in every step such that by the end of the research, the researcher was able to collect only 40% of the issued logbooks. Essentially, having more than 10% of the respondents dropping out of the study has serious effects as the researcher is forced to generalize the results.
Design contamination: Contamination occurs when the two different groups that ought to be measured differently copy one another. In the hospital setup, the control group of nurses can learn about how the other group of nurses is responding and they would respond in a similar manner.
Possible Threats to External Validity
When carrying out a research, some inevitable incidences would force the researcher to generalize the results. The inevitable incidences are threats that make it difficult for the researchers to authenticate their research results to the audiences.
Unique program features: When researchers use a unique program in the analysis, they are likely to generalize the results. Moreover, when researchers use unique program features, they could have problems in describing and convincing the audiences that the research results are genuine.
Experimental arrangements: The experimental effect occurs when the study is only doable with the experimenter. Essentially, the person implementing the study determines the success of the project. Fortunately, in this case, there was no direct contact with the respondents as all the data collection instruments were mailed to the respondents.
Other threats: Other possible threats to external validity include the population validity, pretest and posttest sensitization, and the interaction time length. Whenever the researcher carries out pre-tests and post-tests, cause-effect relationships are likely to occur. The relationships could somewhat influence the researcher to generalize the study findings, which would corrupt the research results.
Conclusion
Indeed, the article makes sense because an exhausted individual is more likely to commit an error than a non-exhausted individual is. The nurses who work for prolonged overtime endanger the patients’ lives. Although the research presents some articulate information, the researcher could have contributed to the lessened number of respondents. It is noteworthy that some respondents would have shied away from writing the exact errors that occurred during their duties, as the logbooks never assured the respondents of the confidentiality of the information given. Essentially, every data collection instrument must assure the respondents of confidentiality of all information given (Franklin, 2012). The return rate of the duly filled logbooks was 40%. This is a clear indication that there was a problem with the data collection instrument. The logbooks were considerably longer, and the respondents had a hard time trying to balance between attending to their duties and filling the logbooks. Therefore, the research could not provide very decisive information and I am somewhat skeptical of the information. The researcher might have to construct shortened logbooks in studies to come. Overall, the article is very useful, as it will raise the eyebrows of the executives in the healthcare systems and they may consider recruiting new nurses. The society will understand that nursing role has a challenge, and they will understand why the nurses are moody at times. Most importantly, the article will provoke the nurses to fight for their rights. Certainly, the article will be of great help to many individuals, and researchers are encouraged to carry out similar researches to enhance the healthcare systems.
References
Flynn, L., Liang, Y., Dickson, G. L., Xie, M., & Suh, D. (2012). Nurses’ practice environments, error interception practices, and inpatient medication errors. Journal of Nursing Scholarship, 44(2), 180-186.
Franklin, M. I. (2012). Understanding research: Coping with the quantitative-qualitative divide. London: Routledge.
Scott, L. D., Hofmeister, N., Rogness, N., & Rogers, A. E. (2010). An interventional approach for patient and nurse safety. Nursing Research, 59(4), 250-258.
Stimpfel, A., Sloane, D., & Aiken, L. (2012). The longer the shifts for hospital nurses, the higher the levels of burnout and patient dissatisfaction. Health Affairs Project, 31(11), 2501-2509.
Trinkoff, A., Geiger-Brown, J., Brady, B., Lipscomb, J., & Muntaner, C. (2010). How long and how much are nurses now working? Too long, too much, and without enough rest between shifts, a study finds. American Journal of Nursing, 106(4), 60-72.