Nurse shortage has been a major challenge to the administrator of Yana hospital. The hospital has experienced diversion of emergency services to other hospitals while patients have had to wait for long periods to get services. This challenge has been attributed to a shortage of healthcare professionals in the facility. Imbalance in the health workforce is a topic that is frequently discussed by the media, scholars, and legislators. Health workforce imbalance is a key problem in both rich and third world nations, as it results in decreased healthcare performance and efficiency (Drennan & Ross, 2019). Frontline health professionals throughout the globe have carried the enormous weight of addressing a worldwide epidemic while consistently providing crucial health services for the past three years. A skill imbalance occurs when the number of skilled employees is required to outgrow the available quantity (Drennan & Ross, 2019). The increased demand for occupational skills in the healthcare sector has been a major blow to the productivity and quality of healthcare services.
Internationally, there is an insufficient supply of health workforce, with low-income nations bearing the worst burden, particularly in Sub-Saharan Africa and portions of Eurasia. The staffing shortage in the healthcare sector was reported to be 6% in 2010, with 1.89 million full-time nurse practitioners in the field and demand of 2 million. If this trend continues, the shortfall is expected to reach 29% by 2025. Nurses account for 75% of all major hospital job openings (Drennan & Ross, 2019). Regardless of the fact that enrollments in entry-level undergraduate nursing increased in the autumn of 2019, reversing a 6-year decline, the population of students in the academic process is still inadequate to satisfy the estimated need for a million new clinicians over the next fifteen years. Nurses are becoming increasingly disgruntled with their jobs. According to a 2010 study, 40% of nursing professionals are unsatisfied with their careers, and one in every three-hospital nurses under the age of 30 plans to quit his or her present position within the following year (Drennan & Ross, 2019). Imbalances, particularly shortages, are said to have a range of negative repercussions, including higher mortality.
Normal Distribution
The normal distribution, like every probability distribution, specifies how a parameter’s values are distributed. It is the most important probability distribution in statistics because it accurately reflects the variability for many natural events. Attributes that aggregate several distinct processes usually exhibit normal distributions. The central theorem, one of the essential characteristics of the normal distribution, is critical in research analysis. This theorem denotes the link between the curvature of the mean and median and the shape of the mean sampling distribution. The theorem also shows that as the size of the sample grows, the mean sampling distribution becomes normal. In the event of a large sample size, the normal distribution is a suitable unbiased estimator. When employed in statistical process control, normal distribution helps establish control limits.
The study will employ the skewness and kurtosis coefficients to establish a normal distribution. This approach helps quantify how much a particular distribution deviates from a normal distribution. The covariance of a dispersion quantifies its uniformity. When the skewness of a distribution is zero, the population distribution is normal. The study will also employ kurtosis to measure the nature of the distribution. Kurtosis compares the girth of a distribution’s tail ends to the tails of the normal distribution. Kurtosis determines whether the statistics are heavy-tailed or light-tailed in comparison to a normal distribution. Statistics with a significant kurtosis have a large proportion of misfits. Finally, the study will use standard deviation to measure the distribution of the statistics. Standard deviation establishes the spread and width of the bell curve. A larger value of standard deviation connotes a more widespread distribution.
Hypothesis
Null Hypothesis
The shortage of healthcare professionals has an adverse effect on the productivity of the healthcare sector.
Alternative Hypothesis
The productivity of the healthcare sector does not depend on healthcare staffing.
Dependent Variable
In this study, the dependent variable will be productivity in the healthcare sector. The amount of time a patient waits for a health treatment after checking in will be considered an indicator of productivity in healthcare. The more they are forced to wait, the more irritated they become. The study will assess the element of patient retention rate as a determinant of productivity in the sector. Patients who are happier are more likely to return when they want medical treatment. This shows a greater level of client satisfaction. On the other hand, a lower or falling patient retention rate indicates a decline in the hospital’s productivity and quality. Client satisfaction will be assessed through customer feedback on their visits. Further, the study will focus on employee turnover as a measure of productivity. Motivated employees will show consistency and punctuality in their services.
Independent Variable
The study will use the shortage of employees as an independent variable. The elements that constitute this phenomenon will be studied and analyzed. The study will analyze gender issues, professional regulations, employee motivation and remuneration, and the capacity of training institutions. Although males are a minority in healthcare, female nurses have special challenges in establishing their right to participate in decision-making, partially since male physicians and/or professional managers almost dominate leadership positions. The study will assess the professional regulations of the healthcare profession. Traditionally, the medical profession has been regulated by a blend of direct government control and, to a considerable part, norms approved by professional bodies. Entry barriers to the medical field can take several forms. Exams to earn licensing are examples, as is the imposition of educational qualifications and a maximum number of academic institutions.
Measures of Central Tendency
A central tendency measure is a specific number that aims to represent a collection of data by determining the central location within that set of data. Scholars use central tendency measurements to discover the usual quantitative value in a group of statistics. The data sets in any group are distributed on a range from the least to the highest value (Kwak & Kim, 2017). Measures of central tendency inform scholars about the location of the center value in a given data. The researcher will use the mean as a measure of central tendency. This approach considers every number in the dataset and thus serves as an accurate representation of the data. The paradox here is that this number almost never exists in the original data. The means of repeated samples collected from the same population tend to be similar. Therefore, the mean is the measure of central tendency that effectively withstand variation across samples.
The median is a statistical metric that identifies the midpoint of an ascending-ordered dataset. The metric separates the dataset’s lower and upper halves. The median function is useful for representing a wide range of data points with a single statistic (Kwak & Kim, 2017). In general, when sorted in a specified order, this metric represents the middle value of a given set of data. The study will use median because extremely big or extremely small numbers do not affect it. Besides, the measure is appropriate when the data involved are ordinal. Median is not affected by any abnormal state of extreme data and is useful in research involving large data sets.
Conclusion
The problem of imbalance in healthcare professionals is diverse and involves huge amounts of data. Evaluating this phenomenon will entail going through huge volumes of data, which will require a proper assessment. Using shortage of professionals as an independent variable and healthcare productivity as the dependent variable is the most appropriate approach and will help unveil the causal factors and remedies to the problem. Besides, the use of a wide range of measures of distribution such as skewness and kurtosis will ensure that the data sets in the study are normally distributed. This approach will guarantee the accuracy of the research findings and analysis. Further, the use of mean and median and measures of central tendency will improve the accuracy of analysis in the study.
References
Manikandan, S. (2017). Measures of central tendency: Median and mode. Journal of Pharmacology and Pharmacotherapeutics, 2(3), 214.
Drennan, V. M., & Ross, F. (2019). Global nurse shortages: The facts, the impact, and action for change. British Medical Bulletin, 130(1), 25-37.
Kwak, S. G., & Kim, J. H. (2017). Central limit theorem: The cornerstone of modern statistics. Korean Journal of Anesthesiology, 70(2), 144.
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