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- NURS 6052 Module 2 Assignment
Identifying Research Methodologies
Student Name
Walden University
NURS 6052
Professor Name
Submission Date
Clinical Issue: Understaffing vs Patient Load
Matrix Worksheet Template
Use this document to complete Part 1 of the Module 2 Assessment, Evidence-Based Project, Part 1: Identifying Research Methodologies.
| Article #1 | Article #2 | Article #3 | Article #4 | |
| This article made a specific focus on the clinical issue of interest: the effect of understaffing versus patient loading in critical care units (Almenyan et al., 2021). The study examines the relationship between the increase in the workload of nurses and the outcomes of patients to understand the effect of understaffing. The results indicate that the higher the nursing workload, the more likely patients are to die, suffer from nosocomial infections, and be admitted to the ER. The conclusions of this article are pertinent to the clinical problem, as it highlights the need for adequate nurse staffing to maintain patient safety and quality of patient care.This study is based on the ethical principles of research: beneficence (do no harm) and non-maleficence (avoid harm). The research supports ethical norms by showing that reducing nursing workload is important for patient safety, nurse well-being, and burnout and job satisfaction. Hence, this article urges healthcare organizations to take a moral and effective approach to tackle staffing challenges. | This article was selected due to its analysis of the clinical problem of interest as it relates to the quality of patient care and nurse burnout during the COVID-19 pandemic (Lasater et al., 2021). The study examines real-time data from hospitals in New York and Illinois to demonstrate the impact of understaffing on patient safety and nurse outcomes in the event of a public health crisis. The article is important because it links under-staffing to poor outcomes such as high nurse burnout rates, higher patient dissatisfaction, and poor safety outcomes, all of which are directly connected to the issue of under-staffing versus patient load.The research highlights the importance of ethical health policy based on staffing for the protection of patients and nurses. The results suggest that safe nurse staffing guidelines should be consistent with the ethical principles of beneficence (promoting well-being) and nonmaleficence (avoiding harm). Appropriate staffing levels are crucial to reduce nurse burnout and to provide high-quality care, both of which are moral obligations in health care. | This article was chosen because it takes a novel approach to analyzing the discrete event simulation (DES), especially the effects of nurse-to-patient ratios on patient workload and treatment quality using discrete event simulation (Qureshi et al., 2019). Understaffing versus patient loads in healthcare is a problem the study addresses, quantifying the effects of different staffing levels on workload and outcomes for patients. The study’s simulation model can also provide healthcare managers with estimates of how the various nurse-patient ratios could impact the delivery of care, which could be used to guide policy decisions.The research also supports the ethical principles of beneficence and non-maleficence as it assists in the making of evidence-based decisions that will have positive patient safety and nurse well-being outcomes for healthcare administrators. The DES model is a way for staff to assess staffing strategies, without any actual risk to patient or nurse, and with ethical considerations in mind. | The article selected was chosen because it delves deeply into the clinical issue of interest, particularly the correlation between nurse understaffing, limited hospital work experience, and in-hospital mortality among nurses (Peutere et al., 2024). The study is based on a longitudinal register-based design to increase insight into the effects of patient load and understaffing on the patient outcome over time. This quantitatively examines the impact of understaffing and inexperienced nurses on deaths, which is important in determining the quality and safety of healthcare.The study highlights the ethical obligations of healthcare institutions with regard to the proper staffing of their medical staff and supplying sufficient training and experience. The findings of the study emphasize the importance of minimizing patient risks and maximizing positive health outcomes through the elimination of understaffing and ensuring experienced nurses are on board, especially the ethical principles of beneficence and non-maleficence. | |
| Brief description of the aims of the research of each peer-reviewed article | This research aims to review and consolidate information on how nursing workload affects ICU patient outcomes. The effect of an increased number of nursing tasks on patient mortality, nosocomial infection, and length of hospital stay will be explored. These findings are used to highlight the significance of nursing staffing in achieving patient safety and the quality of intensive care unit care. The research is designed to encourage hospital officials and decision makers to consider and change staffing based on workload to improve patient care. | Based on the knowledge that the COVID-19 pandemic is in mind, this research was designed to provide empirical evidence regarding the effects of understaffed nurses in chronic hospitals on public health. The study compared the number of nurses per patient in hospitals in New York and Illinois with some nurse-reported outcomes, such as burnout, job satisfaction, and desire to leave. The impact of staffing on patient outcomes, such as patient satisfaction and safety. Thus, the research was designed to shape policy discussion on safe staffing laws in these states. | The purpose of this research is to develop and test a novel nurse-centered DES model that can simulate the effect of the nurse-patient ratio (NPR) changes on nurse workloads and quality of care. The study sought to find the effect of various NPRs (low, medium, and high) on task queue and cumulative walking distance (as indicators of workload) and missed care and task in queue duration (as indicators of care quality). The study was designed to enable healthcare executives to evaluate, in advance, how policy change will affect both their nurse workload and patient care without having to try it and see. | The foremost objective of this study was to investigate whether hospital understaffing and lack of nurse work experience in the day-to-day care of patients are associated with in-hospital mortality based on patient-level data across multiple time periods and a host of hospital-level variables. The study aimed to measure the risk of death due to these exposures during the first 30 days of hospitalisation. The study also sought to find out whether these exposures differed from other patient categories (e.g., older patients and those with comorbidities) and whether they could influence staffing decisions to ensure patient safety, and identify vulnerable patient populations. |
| Brief description of the research methodology used: Be sure to identify if the methodology used was qualitative, quantitative, or a mixed-methods approach. Be specific. | The writers systematically searched, reviewed, and synthesized data from multiple sources such as observation studies, meta-analyses, and empirical studies. This approach to methodology involves a review of previous studies to gain insight into the relationship between nursing workload and poor outcomes of care. The study employs a qualitative approach and focuses on synthesizing the results from various settings and sites to gain insights into the relationship between nursing workload and patient safety and quality of care. | The data for this study were collected through a quantitative research observational methodology on survey data of direct-care registered nurses (RNs) from hospitals in NYS and IL. Before COVID-19, 254 hospitals responded with data on their patient-to-nurse staffing ratios, nurse burnout, staff job dissatisfaction, and satisfaction with patients between December 2019 and February 2020. The methodology employed cross-sectional analysis and statistical models to analyse the relationship between staffing levels and results reported. A quantitative technique was used for the study to quantify staffing ratios and health impacts and to provide statistically significant data to support policy recommendations.. | This study employed a quantitative research methodology employing a discrete event simulation (DES) model dubbed the “Simulated Care Delivery Unit.” The model was developed using Rockwell ARENA, a commercial DES software, to depict the workflows of healthcare professionals. Patient care data, operational logic, and virtual layout were inputs for the simulation, while nurse workload (task queue, cumulative distance traveled) and care quality were outputs. The simulation was done with low (1 nurse: 2 patients), medium (1:4), and high (1:6) nurse-patient ratios. The effects of different NPRs on nurse workload and care quality were examined during 252 shifts (about a year). | This study used a quantitative longitudinal research methodology based on register. It used administrative data from one Finnish hospital district for 254,446 hospital stays from 2013 to 2019. The methodology used Titania for working hours and Auria for clinical patient data to collect patient demographics, admission and discharge dates, and nurse staffing levels. The study examined nurse understaffing based on days with nurse hours below 90% of target hours and inadequate nursing experience based on the proportion of nurses under three years or younger than 25 years. The study examined these exposures and patient mortality using mixed-effects survival models, controlling for age, comorbidities, and weekend admissions. |
| Qualitative review methodology for synthesizing the results of multiple studies on the effect of nursing workload on patient outcomes in the ICU. The writers systematically searched, reviewed, and synthesized data from multiple sources such as observation studies, meta-analysis, and empirical studies. This approach to methodology involves a review of previous studies to gain insight into the relationship between nursing workload and poor outcomes of care. The study employs a qualitative approach and focuses on synthesizing the results from various settings and sites to gain insights into the relationship between nursing workload and patient safety and quality of care. | The study’s extensive analysis, which controls for hospital size, teaching status, and location, increases the findings’ internal validity. Mixed-level logistic regression models allow researchers to adjust for biases, guaranteeing robust and trustworthy relationships between staffing levels and results. This thorough methodology offers policymakers convincing evidence for nurse staffing legislation. | The quantitative DES methodology employed in this study has the strengths of being able to model complicated healthcare systems and anticipate how staffing ratios affect nurse workload and care quality. DES provides for controlled simulation of diverse scenarios, which improves the reliability of the findings by delivering consistent results over several runs. Due to its realistic design, the model’s input data from patient care records and established task descriptions reinforce the results’ validity.The methodology also analyzes staffing situations without harming patients or healthcare personnel, making it a safer and more ethical alternative to real-life research. The study makes the model more relevant to clinical settings by utilizing historical data from a big urban academic health institution. The DES model’s ability to assess task queues and missed care gives a holistic perspective of how staffing changes affect nurse workload and patient care quality. | The quantitative level methodology’s strengths include using large-scale, patient-level data across seven years, which improves reliability and validity. Administrative data, which includes staffing and patient characteristics, reduces self-reported data biases and improves validity.The methodology’s survival analysis models allow for time-dependent exposures and outcomes, boosting estimate accuracy. Adjusting for patient demographics, comorbidities, and unit-specific characteristics strengthens the study’s validity and generalizability. | |
| General Notes/Comments | It underlines that the nurse-to-patient ratio is typically used to quantify workload, but it does not completely convey the complexity of nursing staff needs. The authors propose a more holistic method that incorporates patient severity, support personnel availability, and organizational context when calculating workload. T The article emphasizes the ethical requirement for healthcare companies to guarantee adequate staffing for safe, high-quality treatment. | This article analyzes how chronic nurse understaffing endangers public health, especially during pandemics like COVID-19. It shows that limited staffing causes negative results for nurses and patients. The study shows that understaffing increases nurse burnout, job discontent, and desire to leave the field, which might worsen workforce shortages. The article is also significant for understanding how staffing rules affect healthcare delivery, especially in high-risk and high-pressure contexts like ICUs. Healthcare institutions have an ethical obligation to create safe working conditions for nurses so they can offer high-quality treatment without sacrificing their health. | This article shows how Discrete Event Simulation (DES) can anticipate healthcare staffing policy changes. The study’s novel approach to modeling gives insights for healthcare managers, policymakers, and academics looking to maximize staffing levels and care quality. The study emphasizes the need to include human factors engineering in healthcare decision-making to balance efficiency, worker safety, and patient care. The cost-effective and safe DES model allows nurses and patients to make better-informed decisions without risking their safety. | This article strongly suggests that nurse understaffing increases in-hospital mortality, particularly in comorbid patients. According to the study, limited nursing expertise was most hazardous to individuals with complicated health problems, including multiple comorbidities. These findings imply that healthcare managers and policymakers should boost staffing and distribute experienced nurses to reduce patient risk and improve service quality. However, the lack of data on patient severity upon admission and staffing differences within hospital units or shifts are constraints. |
References
Almenyan, A. A., Albuduh, A., & Al-Abbas, F. (2021). Effect of Nursing Workload in Intensive Care Units. Cureus, 13(1). https://doi.org/10.7759/cureus.12674
Lasater, K. B., Aiken, L. H., Sloane, D. M., French, R., Martin, B., Reneau, K., Alexander, M., & McHugh, M. D. (2021). BMJ Quality & Safety, 30(8), 1–9. https://doi.org/10.1136/bmjqs-2020-011512
Peutere, L., Pentti, J., Ropponen, A., Kivimäki, M., Härmä, M., Krutova, O., Ervasti, J., Koskinen, A., & Virtanen, M. (2024). International Journal of Nursing Studies, 150, 104628. https://doi.org/10.1016/j.ijnurstu.2023.104628
Qureshi, S. M., Purdy, N., Mohani, A., & Neumann, W. P. (2019). Journal of Nursing Management, 27(5), 971–980. https://doi.org/10.1111/jonm.12757
