Write a nursing philosophy paper that clearly reflects your personal philosophy of nursing. The paper should include the identification and examination of your own definition of nursing’s meta-paradigms: nursing, person, health, and environment.

Identify and discuss one other concept that you believe should be a part of nursing’s meta-paradigm (e.g. social justice, caring, technology, complexity, spirituality, culture, interdisciplinary practice, sustainability of resources, efficiency, safety… you may use another concept if it better complements your philosophical perspective) .

Discuss how your definition of the meta-paradigm concepts influences how you understand and accomplish your work as a nurse. Integrate a minimum of three examples from your work into your paper that captures the essence of your philosophy.

Parts of this paper can be written in the ‘first person since it is your personal philosophy of nursing. However, when you are defining and discussing the meta-paradigms, avoid using the first person. (See the Philosophy of Nursing in the RN to BSN Handbook for an example of how to write these sections in a more academic tone or third-person.)

Understanding social factors and their impact on curriculum development is critical for a curriculum developer. Critically analyze three social factors that can influence the curriculum and discuss how you will address them when developing your curriculum

Protection of Human Participants in Epidemiological

Ethical issues must be considered when carrying out research that involves human subjects. There are several ways in which human participants must be protected to ensure they are free from risks and have a possibility of benefiting from the study. Ethical standards have been set, which researchers are expected to meet.

Describe the various ways in which epidemiology research participants must be protected

Watch Types of Data: Nominal, Ordinal, Interval/Ratio – Statistics Help (6:19), then answer the following discussion questions.

https://www.youtube.com/watch?v=hZxnzfnt5v8

Types of Data: Nominal Ordinal Interval/Ratio
Data is central to statistical analysis. When we wish to find out more about a phenomenon or process we collect data. Usually, we collect several measures on each person or thing of interest. Each thing we collect data about is called an observation. If we are interested in how people respond, then each observation will be a person. OR an observation could be a business or a product, or a period in time, such as a week. Variables record the measurements we are interested in. Age, sex, and chocolate preference can all be stored as variables. For each observation, we record a score or value for each of the variables. When we store this data in a spreadsheet or database, each row corresponds to a single observation and each column is a variable.

Level of Measurement
The level of measurement used for a variable determines which summary statistics, graphs, and analyses are possible and sensible. The Nominal level is the most basic level of measurement.
Nominal is also known as categorical or qualitative. Examples of nominal variables are sex, preferred type of chocolate, and color. These are descriptions or labels with no sense of order.

Nominal values can be stored as a word or text or given a numerical code. However, the numbers do not imply order. To summarise nominal data we use a frequency or percentage. You can not calculate a mean or average value for nominal data.

The next level of measurement is Ordinal.
Examples of ordinal variables are rank, satisfaction, and fanciness! Ordinal variables have a meaningful order, but the intervals between the values in the scale may not be equal. For example, the gap between first and second runners in a race may be small, whereas there is a bigger gap between second and third. Similarly, there may be a big difference between satisfied and unsatisfied, but a smaller difference between unsatisfied and very unsatisfied.

Like Nominal data, ordinal data can be given as frequencies. Some people state that you should never calculate a mean or average for ordinal data. However, it is quite common practice, particularly in research regarding people’s behavior to find mean values for ordinal data. You should be careful if you do this to think about what it means and if it is justifiable.

The most precise level of measurement is interval/ratio.
This label includes things that can be measured rather than classified or ordered, such as number of customer’s weight, age and size. Interval ratio data is also known as scale, quantitative or parametric. Interval/Ratio data can be discrete, with whole numbers or continuous, with fractional numbers. Interval/Ratio data is very mathematically versatile. The most common summary measures are the mean, the median, and the standard deviation. The way data should be represented in a graph or chart depends on the level of measurement.

Nominal data can be displayed as a pie chart, column or bar chart or stacked column or bar chart. In most cases, the best choice for a single set of nominal data is a column chart. Ordinal data must not be represented as a pie chart, but is best shown as a column or bar chart. Interval/ratio data is best represented as a bar chart or a histogram. For these, the data is grouped. Box plots illustrate the summary statistics for a variable in a neat way. Data that occurs over time is best displayed as a line chart.

Here is an example using different types of data:
Helen sells choconutties. Helen is interested in developing a new product to add to her line of choconutties. She develops a questionnaire and asks a random sample of 50 of her customers to fill it out. She asks them their age and sex, how much they spend on groceries each week, how many chocolate bars they buy in a week, and which they like best out of dark, milk, and white chocolate. She asks them how satisfied they are with choconutties: very satisfied, satisfied, not satisfied, very unsatisfied. And she asks them how likely they are to buy a whole box of 10 packets of choconutties.

Helen enters the data in a spreadsheet. Each row has responses from one customer. Each column contains the measurements or scores for one variable. The type of chocolate preferred is nominal data. This can be shown in a pie chart or bar chart.

We can summarise by saying that 46% of customers prefer Dark chocolate, 40% prefer milk chocolate, and 14% prefer white chocolate. The measures of satisfaction and likelihood are ordinal level data. These should not be shown in a pie chart. The values should be put in a logical order in a column chart. We could say that 32% are very satisfied with choconutties and 72% of people are satisfied or very satisfied and 72% of people are satisfied or very satisfied.
The average satisfaction score comes to 2.06, which could be interpreted as satisfied. However, it is debatable whether it is sensible to calculate a mean satisfaction score. Age, the amount spent on groceries, and the number of chocolate bars are all interval/ratio data. These can be displayed on bar charts or histograms. We can say that for the customers in the sample, the mean age is 38 years, the mean amount spent on groceries is $192, and the mean number of chocolate bars bought per week is 3.3.

These are all meaningful summary statistics. The type of analysis that is sensible for a given dataset depends on the level of measurement. You can find out more about this in the video, “Choosing the test”.

Please answer these question

1. What scale would brands of soft drinks be and why?
2. What scale is blood glucose levels and why?
3. What scale is rank-ordered preferences for brands of insulin and why?
4. If the correlation coefficient between stress and eating problems is r = 0.74, what does this value tell you about the direction of the relationship and the strength of the relationship?