Read a selection of your colleagues’ responses and respond to at least two of your colleagues on two different days in one or more of the following ways:
- Ask a probing question, substantiated with additional background information, evidence, or research.
- Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
- Offer and support an alternative perspective using readings from the classroom or from your own research in the Walden Library.
- Validate an idea with your own experience and additional research.
- Suggest an alternative perspective based on additional evidence drawn from readings or after synthesizing multiple postings.
- Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.
- 1.
Inferential statistical analysis is a method used to conclude a specific population based on a sample of data from that group. The research question I have chosen is: Will providing onsite knowledgeable psychologists in the emergency room / acute care settings, improve patient outcomes through individualized care?
For this discussion post, I chose an article that applies the inferential statistical analysis method as well as other methods. “These basic methods include descriptive statistics, inferential methods for comparing groups, methods for repeated measurement, correlation coefficient methods, and multivariable regression methods” (Nieminen, Kaur, 2019). Since my topic is about mental health and its relation to educated therapists, I chose this article to a research to reflect that. This study looks at how study designs and data analysis in psychiatry studies have changed over the past 22 years. Over the past two decades, statistical practices in psychiatric journals have shifted. While traditional methods of testing statistical significance were common in 1996 and 2018, there was a rise in the use of more intricate techniques like multivariable regression models and multilevel modeling in 2018. However, computationally complex procedures such as data mining or machine learning were not embraced by psychiatric researchers during this time (Nieminen, Kaur, 2019). This article utilizes inferential statistical analysis by comparing different data analysis methods in psychiatric studies over a 22-year period. Through a review of 320 published in prominent psychiatric journals, the study looks at changes in statistical practices, such as the adoption of more complex techniques like multivariable regression models and multilevel modeling. Using inferential statistical analysis did strengthen this study because these findings indicate an evolution in statistical intensity over time, highlighting the importance of readers possessing statistical expertise to critically evaluate research findings. This underscores the need for enhanced statistical education in psychiatric undergraduate and postgraduate programs. This study strengthens evidence-based practice by thoroughly analyzing key variables like age, education, ethnicity, employment, and income. Detailed statistics provide insights into the population's demographics and socioeconomic status, informing decision-making in healthcare and policy. Emphasizing descriptive analysis aligns with evidence-based practice, aiding the interpretation and utilization of data for informed interventions.
Reference:
Gray, J. R., & Grove, S. K. (2020). Burns and Grove’s the practice of nursing research: Appraisal,
synthesis, and generation of evidence (9th ed.). Elsevier. Chapter 25, “Using Statistics to
Determine Differences” (pp. 687–698)
Nieminen, P., & Kaur, J. (2019). Reporting of data analysis methods in psychiatric journals:
Trends from 1996 to 2018. International journal of methods in psychiatric research,
2. The study delves into the impact of shift work, particularly quick return schedules with short rest periods, on nurses' well-being and burnout (Hatukay et al., 2024). It highlights existing research on the negative effects of such schedules on stress, sleep, and ultimately burnout among nurses. The study proposes a moderated-mediation model to explore how quick return schedules influence burnout, drawing from theories like the job demands-resources model and the conservation of resources theory (Buchvold et al., 2019). These theories suggest that high demands and limited resources, such as short rest periods, can lead to burnout unless countered by factors like high motivation, which acts as a psychological resource for nurses (Min et al., 2019). Given the prevalence and serious consequences of burnout in shift-working nurses, understanding these underlying mechanisms is crucial.
Summary of the Study:
In the study by Hatukay et al. (2024), the research study involved 79 nurses, with approximately half being women (n = 40, 50.6%). The average age of the participants was 37.3 years (standard deviation [SD] = 10.0), and their average nursing experience was 11.8 years (SD = 11.2). The study collected data across a total of 369 shifts, categorized into morning, evening, and night shifts, with 15.4% of shifts classified as quick return shifts. The study measured variables such as sleep duration, motivation levels, burnout levels, workload, age, years of experience, and gender. Inferential statistics, including correlation analyses and moderated-mediation models, were utilized to analyze the data.
Data Sources:
The data for this study were likely collected through surveys, self-reported measures, and possibly objective measures (e.g., shift schedules, sleep duration records). The participants' demographic information, along with their responses to various scales measuring burnout, motivation, and other variables, were likely collected through questionnaires or interviews.
Inferential Statistic(s) Used:
The inferential statistics used in the study included correlation analyses to examine relationships between variables (e.g., quick return schedules, sleep duration, motivation, burnout, workload, age, years of experience, gender) and moderated-mediation models to explore the interplay and predictive value of these variables on sleep duration and burnout.
Associated Findings:
The study found a moderate negative correlation between quick return schedules and sleep duration, indicating that shorter rest periods between shifts were associated with reduced sleep duration. However, no statistically significant association was found between quick return schedules and burnout. Weak negative correlations were observed between motivation and burnout levels, as well as between motivation and engagement in quick return schedules. The moderated-mediation models revealed that engagement in quick return schedules was associated with shorter sleep duration, while longer sleep duration and greater motivation were associated with lower burnout levels.
Evaluation of the Study:
The purpose of this research study was to investigate the relationships between shift schedules, sleep duration, motivation, and burnout among nurses. The study's value lies in its exploration of factors contributing to nurse burnout, including work schedules and individual characteristics like motivation and sleep patterns. By using inferential statistics such as correlation analyses and moderated-mediation models, the study was able to examine complex relationships and identify potential predictors of burnout.
Using inferential statistics strengthened the study's application to evidence-based practice by providing statistical evidence of associations and effects. For example, the correlation analyses demonstrated the relationship between quick return schedules and sleep duration, as well as between motivation and burnout levels. The moderated-mediation models further elucidated how these variables interacted to influence sleep duration and burnout among nurses. This statistical approach allowed for a more nuanced understanding of the factors contributing to nurse burnout, informing evidence-based interventions and policies aimed at improving nurse well-being and patient care outcomes.
Overall, the study's use of inferential statistics enhanced its rigor and contributed valuable insights to the topic of nurse burnout, highlighting the importance of addressing work schedules, sleep quality, and motivational factors in promoting nurse resilience and reducing burnout rates.
References
Buchvold, H. V., Pallesen, S., Waage, S., Moen, B. E., & Bjorvatn, B. (2019). Shift Work and Lifestyle Factors: A 6-Year Follow-Up Study Among Nurses. Frontiers in Public Health, 7. https://doi.org/10.3389/fpubh.2019.00281
Hatukay, A. L., Shochat, T., Zion, N., Baruch, H., Cohen, R., Azriel, Y., & Srulovici, E. (2024). The relationship between quick return shift schedules and burnout among nurses: A prospective repeated measures multi-source study. International Journal of Nursing Studies, 151, N.PAG–N.PAG. https://doi.org/10.1016/j.ijnurstu.2023.104677
Min, A., Min, H., & Hong, H. C. (2019). Work schedule characteristics and fatigue among rotating shift nurses in hospital setting: An integrative review. Journal of Nursing Management, 27(5), 884–895. https://doi.org/10.1111/jonm.12756