The Simulation Design Score Affects The Participant Clinical Judgment Score Essay

The following is a critical reflection of my performance during an objective structured clinical exam (OSCE), as part of my training for the role of Psychological Wellbeing Practitioner (PWP). An OCSE is an assessment technique whereby a student demonstrates their competence under simulated conditions (Fidment 2012). For this OSCE, my competence in undertaking a treatment session with a patient was under examination. The treatment session I undertook involved discussing the patient’s progress with behavioural activation (BA), a cognitive-behavioural intervention used for people experiencing depression (Richards and Whyte 2011).

In research, there are several variables that can change depending on the circumstances. Coming up with an operational definition of those variables ensures that all reading the research understand “the procedures used to measure or manipulate” them. (Cozby & Bates, 2012). When we’re looking at more than one variable, we must be concerned with how the variables relate to each other. These relationships can be defined as negative linear, positive linear, curvilinear, or no relationship. The two ways we can study these relationships are through non experimental and experimental methods. Non experimental does not involve any direct manipulation of the variables as opposed to experimental which involves direct manipulation

Question 5: Now that you have had a chance to apply your guiding statement to several simulations, on a scale of 1 to 10 (1

The Barbie Bungee lab was conducted in order to find the association between the amount of rubber bands and the distance the Barbie bungeed. Before performing the final experiment, the group conducted an initial investigation to get data that could be analyzed to examine the comparison from the amount of rubber bands to the length Barbie was able to bungee. In the investigation rubber bands would gradually be added one by one starting at two rubber bands. Each time a rubber band was added, three trial bungees were done and the lengths the barbie dropped were recorded. Using data collected from our background investigation, the group used excel to create a sheet displaying the data in a table, a graph showing the correlation constant, the line of best fit. The line of best fit was in slope-intercept form (y=mx+b) where y represents the length of the trial average; m represents the slope

It helps clarify relationsips between variables that cannot be examined by other methods and allows prediction

-The Institute of Medicine recommends simulation as a method of teaching interventions in high risk situations.

"There are several different kinds of relationships between variables. Before drawing a conclusion, you should first understand how one variable changes with the other. This means you need to establish how the variables are related - is the relationship linear or quadratic or inverse or logarithmic or something else" ("Relationship Between Variables ", n.d)

Sometimes failure can really be the best teacher. This was the case with the Lakeview Regional Hospital Simulation Exercise. During the simulation, I learned a lot about working with a team, knowing when to stand firm and when to compromise. I have been a part of plenty of projects, but sometimes I can be a little lost when it comes to the healthcare aspects of things. It was during these times that I looked to my teammates to assist with filling in some valuable blanks about the healthcare environment. I do have a lot of experience with introducing technology, training and media relations. It was during these parts of the simulation that I could really lend a helping hand. The simulation illustrated the importance of buy-in amongst the implementation team as well as other members of the entire organizations. It showed that there will always be some inherent resistance, but that doesn’t mean that change is impossible as long as there is some flexibility.

2. To determine how much effect each of the independent variables has on the dependent variable, we examine the correlation coefficient for each of the independent variables. The higher

I started simulation by testing all the possible outcomes from challenge 1 to 5 and used the best scenario from one simulation to the other step by step and drew the conclusion at the very end.

This essay will discuss a clinical skill in which I have become competent in practicing. I will use a reflective model to discuss how I have achieved the necessary level of competence in my nurse training programme. The reflective model I have chosen to use is Gibbs model (Gibbs 1988). Gibbs model of reflection incorporates the following: description, feelings, evaluation, analysis, conclusion and an action plan (Gibbs 1988). The model will be applied to the essay to facilitate critical thought, relating theory to practice where the model allows. Discussion will include the knowledge underpinning practice and the evidence base for the clinical skill. A conclusion to the essay will then be given which will discuss my reflection skills, acknowledge my competence and show my personal and professional development.

2. Use simple regression to estimate the salary cost function for Delta Airlines. Comment on the statistical validity and significance of your results. What are the

Linear regression including trend lines, regression formals, and the coefficient of determination for each pair of variables visualized in the scatter diagrams.

The objective of my experiment is to identify the relationship between the definite boiling points of distilled water, and water that has been mixed with our everyday table salt. In this experiment, the Independent Variable will be the amount of salt I have added to the constant( our water), and the Dependent Variable will be the boiling temperature that it reaches.

In Part 1, I conducted a statistical experiment to determine if a linear relationship exists between each of my independent variables and my dependent variable, which were biological in nature. My independent variables were height (inches) and age (years). My dependent variable was weight (pounds). The goal of my experiment was to observe a linear correlation between an 18-22 year-old male’s height and weight. I collected my data by randomly selecting a sample size of 25 male individuals who attend Montclair State University. The best method of prediction would