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please include at least 2 references each and do not make each introduction sound alike

respond to 
the four colleagues 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.



Traumatic brain injuries (TBI) are caused by “a forceful bump, blow, or jolt to the head or body, or from an object that pierces the skill and enters the brain” (National Institute of Neurological Disorders and Stroke, 2023) although not all injuries result in traumatic brain disabilities. TBIs can cause temporary, short-term problems with brain function or they can also cause more severe and permanent damage to the brain which can lead to permanent disability and even death. Helmet use can help in the prevention of severe injury caused by TBI in high-risk activities. In fact, current research states “TBIs occurred in 47.6^ of patients no wearing helmets by comparison to only 19.1% of patients wearing helmets” (Dodds, et al., 2019).  This study will be used to conduct research to gain evidence related to age specific helmet use and TBI incidence. It will allow for the creation of helmet education and TBI prevention initiatives to improve patient outcomes.

Research Question –

Does the use of helmets in the pediatric population significantly reduce the incidence of traumatic brain injuries (TBIs)?

Null Hypothesis – There is no significant difference in the incidence of traumatic brain injury (TBI) between children who use helmets and those who do not use helmets in high-risk activities.

Alternate Hypothesis – There is a significant difference in the incidence of traumatic brain injury (TBI) between children who use helmets and those who do not use helmets in high-risk activities.

Dependent Variable – Incidence of TBI in pediatric populations

Independent Variable – Use of Helmets

I predict the higher the helmet usage is the lower the incidence of TBI will be in pediatric populations. I believe this will be the case because of the countless research done on helmet use and motorcycles or helmet use and the prevention of TBIs. I found a lack of research in areas of sledding/bike riding and pediatrics and thought It would be interesting to have this be a research topic.


Factors that might affect the outcome of the study include ..

· Age – accidents and injuries occur in children of all ages; younger children are most prone to injury, but teenagers or older children are more likely to engage in risky behavior without helmets.

· Type of activity – The risk of TBI depends on the activity or sport. High contact sports like football, bicycling, sledding all pose a higher risk compared to non-contact sports.

· Helmet use compliance – There are a variety of reasons kids don’t wear helmets especially older kids; they don’t want to see uncool, they don’t want to be made fun of or they just don’t like them leading to poor compliance and higher risk for TBIs.

· Helmet quality and fit – poorly fitting low-quality helmets may not provide the protection needed for the type of activity in which is being engaged leading to higher risk of TBI.

These factors can affect the outcome of the study and should be discussed prior to the start conducting the research. Helmet use is very important in the prevention of TBIs and this research should show us that.


Quantitive  Analysis and Improvement: Correlations

 Correlation is employed to gauge the degree of association or relationship between two variables (Schober et al., 2018). The measure of correlation is the correlation coefficient. Regression, conversely, is a forecasting technique employed to ascertain the predictability of a variable using another variable or, instead, to what extent one variable predicts or determines another (Schober et al., 2018). Correlation studies are advantageous because they allow the researcher to collect more data than would have ordinarily been the case for other studies. Their findings can easily apply to everyday situations because correlational studies are not conducted in a controlled laboratory setting but in real-world environments. It also gives the strength and direction of the relationship between two variables (Schober et al., 2018). For instance, the variables move in different or opposite directions if the correlation coefficient is negative.

Medication Errors

Unfortunately, healthcare system errors still happen at frightening rates. .A medical mistake occurs when an omission or failure to execute a planned action occurs. Stated otherwise, it is an execution mistake. It is achieving a medical goal by employing a suitable plan. Medical mistakes are avoidable negative consequences of treatment, regardless of whether the patient suffers injury or no harm (Jin et al., 2018). Medication mistakes bring on many adverse drug events that have a detrimental impact on patient health. In addition, it is now a significant health burden that accounts for the majority of medication side effects in hospitalized patients. Medical errors can result from misdiagnoses, inappropriate prescriptions, dose calculations gone wrong, unsuitable drug distribution procedures, problems related to drugs and drug-device interactions, and poor clinician-to-clinician communication (Jin et al., 2018). Medication errors put people in danger and frequently cost the organization money.

Research Question

Is there a relationship between the workload of nurses and the occurrence of medication errors in hospital settings?

Null Hypothesis (H0)

There is no significant relationship between nurse workload and medication errors.

Alternative Hypothesis (H1)

There is a significant relationship between nurse workload and medication errors.



Dependent Variable:
 Medication errors (frequency or severity)


Independent Variable:
 Nurse workload (measured by patient-to-nurse ratio, hours worked, or workload intensity)


Anticipating the outcome of this investigation, we hypothesize a direct correlation between nurse workload and the incidence of medication errors within healthcare settings. As the demands placed upon nurses intensify, whether through an elevated patient-to-nurse ratio or extended work hours, medication errors are likely to escalate in tandem. This anticipated association intertwines with a web of multifaceted factors, setting the stage for a convoluted interplay.

The augmentation of nurse workload, a foreseeable consequence, can sow the seeds of fatigue and stress while sapping their cognitive resources (SHoHani & Tavan, 2018). The complexity of this strain network may have negative consequences, gradually diminishing the accuracy of medication administration (Tawfik et al., 2018). The looming shadow of heightened workload might shroud nurses, impeding their ability to maintain the necessary vigilance and accuracy while navigating the labyrinthine corridors of medication administration.

Moreover, the ticking clock, an ever-present adversary in the healthcare environment, imposes a relentless cadence upon nursing tasks. The neglect of meticulous checks and verifications required for error prevention may occur due to time constraints. This accelerated pace, an inexorable consequence of the mounting workload, could precipitate oversights and shortcuts, breeding an environment ripe for errors to flourish (Grove et al., 2020).

The envisioned relationship between nurse workload and medication errors is a complex issue. The stresses and strains caused by higher workloads are balanced to act as catalysts. The stress influences the accuracy and consistency required for an error-free healthcare organization (SHoHani & Tavan, 2018). The adverse impact of time constraints, exacerbated by the impact of workload, can pave the way for inadvertent errors, increasing the likelihood of pharmaceutical errors. The complex dynamics between workloads, time constraints, and their consequences for the healthcare facility paint a picture that deserves scrutiny and investigation (Tawfik et al. event, 2018).

Other factors

Several variables related to prior workload may influence medication errors in the health care setting. As for the appearance, the complexity of understanding the conditions plays a notable role. When understanding conditions is complex, healthcare providers may have difficulty adjusting medications accurately. Additionally, inadequate levels of preparation or staffing can contribute to errors (SHoHani & Tavan, 2018). Nurses require comprehensive preparation and adequate staffing to perform their duties effectively. Infiltration into healthcare organizations is also dangerous because it can disrupt the focus needed for a particular organization. Introducing modern solutions can increase the plausibility of errors due to the needs of nature.

Additionally, organizational variables, such as access to innovation, support frameworks, established approaches, and successful communication within the healthcare team, fundamentally affect errors (SHoHani & Tavan, 2018). Adequate mechanical support, clear layout, and consistent communication channels improve the system’s ability to avoid errors. On the other hand, deficiencies in these areas can increase the likelihood of failure. Understanding and considering these various variables comprehensively is essential to understanding the confusing relationship between healthcare workload and medication errors in the healthcare setting.



Problem Description

Medication errors and nurse staffing are the healthcare issues I chose for the correlation discussion. Medication mistakes are still a significant source of stress for the American healthcare system. It is among the main factors contributing to avoidable fatalities (Tsegaye et al., 2020). It is not always easy to pinpoint the reasons behind drug mistakes and put preventative measures in place to deal with them. Medication mistakes have been recognized as one area needing improvement (Gray & Grove, 2020). The five rights of drug prescription, the appropriate patient, medication, timing, dose, and route, are perceived as broken by medical professionals. Advanced practice nurses should incorporate each of the five rights into pharmaceutical prescriptions to ensure the safe administration of medication. We must ensure the drug contains the appropriate paperwork, action, form, and reaction (Hanson & Haddad, 2022). Many healthcare institutions still need an adequate workforce. An increase in medical mishaps has been linked to a scarcity of nurses. Keeping a highly healthy environment that honors nurses’ values is one of the measures for resolving medication mistakes. Medication errors need quality improvement and should be handled in a highly healthy work environment. Medication errors are important primary indications of high-quality care that healthcare organizations should consider.

The focus of this study is to investigate the potential relationship between nurse-to-patient ratios and medication administration errors within acute care hospitals. Nurse-to-patient ratios represent the number of patients assigned to each nurse. At the same time, medication administration errors encompass deviations from the intended medication regimen, including dosage, timing, or administration route errors (Tsegaye et al., 2020).

The research question is: “What is the correlation between nurse-to-patient ratios and medication administration errors within acute care hospitals?”.

Research Question

What is the correlation between nurse-to-patient ratios and medication administration errors within acute care hospitals?


Null Hypothesis (H0)

No significant correlation exists between nurse-to-patient ratios and medication administration errors within acute care hospitals.

Alternate Hypothesis (H1)

 A significant correlation exists between nurse-to-patient ratios and medication administration errors within acute care hospitals.


Dependent Variable

Medication administration errors (frequency or rate of mistakes).

Independent Variable

Nurse-to-patient ratios (the number of patients assigned to each nurse).

Expected Relationship

Negative correlations are expected between medication administration errors and nurse-to-patient ratios. It is hypothesized that medicine administration mistakes will decline as nurse-to-patient ratios improve, implying reduced patient loads per nurse (Griffiths et al., 2020). This prediction aligns with the theory that lower nurse-to-patient proportions enable nurses to spend more time with each patient, lowering the risk of drug delivery mistakes. When nurse-to-patient ratios are higher, variables including workload, weariness, and time restrictions brought on by heavy patient loads may play a role in errors (McHugh et al., 2021).

Other Factors That Might Affect the Outcome

The outcome might be affected by several variables, including the patient population’s complexity, the training and experience levels of the nurses, and others. For instance, more experienced nurses may be able to manage larger nurse-to-patient ratios than less experienced ones (Driscoll et al., 2018). Even when nurse-to-patient proportions are ideal, drug administration mistakes may sometimes occur due to the complexity of patient circumstances. It is essential to account for or control these variables throughout the investigation to accurately assess the association between nurse-to-patient ratios and medication delivery errors.


Main Question Post:

The selected problem that could be explored using correlational statistics is the association between sleep quality and symptoms of depression or anxiety. This is a bidirectional problem, implying that each can influence the other. Poor sleep quality increases one’s susceptibility to developing signs of anxiety and depression (Oh et al., 2019).

Research Question: Is there a correlation between sleep quality and symptoms of depression or anxiety among individuals with mental health disorders?

Null Hypothesis (H₀):

There is no significant correlation between sleep quality and symptoms of depression or anxiety among individuals with mental health disorders.

Alternative Hypotheses (H₁):

The alternative hypothesis can be two-tailed or one-tailed based on the specific research question and the expected direction of the relationship.

Independent Variable: Sleep quality (e.g., measured using a standardized sleep questionnaire).

Dependent Variable: Symptoms of depression or anxiety (e.g., measured using validated scales such as the Patient Health Questionnaire-9 (PHQ-9) for depression or Generalized Anxiety Disorder 7-item (GAD-7) scale for anxiety).

My prediction for the expected relationship is a negative relationship, implying that higher sleep quality is linked to reduced symptoms of depression or anxiety among people with mental health disorders.

This type of relationship might exist mainly due to biological mechanisms. Sufficient sleep is essential for optimal brain function, stress management, and emotional regulation. Disruptions of sleep can impact neurotransmitter regulation and the stress response system, leading to signs of anxiety and depression (Okun et al., 2018). 

The other factors affecting sleep quality and mental health symptoms include medical conditions or comorbidities such as chronic pain and sleep disorders. Medications, substances (e.g., caffeine, alcohol), or substance withdrawal can influence sleep patterns and mental health symptoms (Zhang et al., 2018).

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