The assignment involves researching a real-life project from Western Australia. The Fleet Base West Low Level Bridge Repair won the Australian Institute of Project Management’s WA Winner Award in 2018. HMAS Stirling is the Royal Australian Navy’s primary base on the west coast and is located on Garden Island, 5km off the WA coast. The 305m low-level bridge is located at the mainland end of the causeway which links the island to Point Peron. The bridge comprises a reinforced concrete deck supported by a steel structure on steel piles, providing a two-lane roadway. The abutments are reinforced concrete and steel sheet piling. There have been various repair works conducted since its commission in 1972, including most recently in 2007/2008. Poor construction and exposure to an aggressive marine environment accelerated deterioration of the bridge, posing a risk to base operations, necessitating urgent repairs. For the assignment, you should access publicly available information to create ‘exemplar’ project quality and risk documentation. You do not need to include specialist engineering information or detailed project data, but your quality and risk management plans should reflect the main project management and site/client issues that could reasonably be perceived as being relevant when the project was in the planning phase.

There are two types of significance used to interpret research studies – statistical significance and clinical significance. They are not the same thing. One answers the question, Are the statistical results due to random chance? and the other answers the question, So what? Will the results matter to our patients?
significance definitionPhoto credit (c) Shutterstock.com
Significance means “the quality of being important.” Importance is a value judgment, right? Though value judgments are considered subjective, there are elements of the “thing” being appreciated that help one assign the characteristic of significance. In research, we attribute importance or worth to research findings according to accepted, albeit sometimes arbitrary, conventions.
Significant, in terms of statistics, is defined as “probably caused by something other than mere chance.” Researchers proclaim a study finding to be “statistically significant” or not, depending on whether their research result is less than the a priori alpha level set before the study commenced.
The alpha level is (supposed to be) formed or conceived beforehand (i.e., a priori). “The term [a priori] usually describes lines of reasoning or arguments that proceed from the general to the particular… a priori knowledge is knowledge that comes from the power of reasoning based on self-evident truths” (https://www.merriam-webster.com/dictionary/a%20priori).
Statistical significance has to do with the likelihood that a research result is true (i.e., a real effect of the intervention) and not merely a matter of chance.Clinical significance is a subjective interpretation of a research result as practical or meaningful for the patient and thus likely to affect provider behavior.(Oh, just an FYI — other than identifying the probability of a research result as a function of chance, p-values don’t tell you very much. But more on why in a future post!)
The Effect of Sample Size on Statistical SignificanceStatistical significance is a function of sample size. If you have a large enough sample size, almost anything can be found to be statistically significant!
Research costs time, effort, and money. Researchers are trying to show relationships between or among their variables of interest – but to do that they need an adequate sample of representative subjects.
If the sample size is not large enough, random error is increased, and the results may not show significant differences (even if the intervention really works better than the SOC) because there are not enough subjects to show that difference. A small sample size is a major reason for making this Type II error.
You can also have studies that have too much power. The problem with very large sample sizes is that very small, statistically significant differences between the research groups can be found. These statistically significant results may not necessarily be clinically significant, though.
A Priori Sample Size Estimation: Researchers should do a power analysis before they conduct their study to determine how many subjects to enroll. Power analysis, precision estimation, or sample size calculations are based on a number of factors, which we won’t go into in this post, but include the type of study they are conducting (cross-sectional, survey, case-control, clinical trial, etc.), significance level, power, effect size, and standard deviation or event rate (Hayat; 2013; Heavey, 2015; Malone, Nicholl, & Coyne, 2016). Studies with samples that are large are expensive to conduct, so funding, the resources available, available population, time, and other factors will also be considered by the researcher when deciding on the final sample size.
“Statistical power is the probability of making a correct decision, namely, that of rejecting the null hypothesis when it is actually false” (Hyatt, 2013, p. 944).The larger the sample size, the greater the power of the study to find a difference if one really exists.Research Sticking PointsShort break before we go on to clarify some commonly misunderstood points. I’m labeling these tougher or complicated research concepts as research sticking points. Here are a few related to our topic.
Research Sticking Point 1: Many students don’t quite get how researchers determine whether their research results are statistically significant or not. Basically, the researchers conduct their experiment and enter their data into a statistical program, like IBM’s SPSS Statistics. When they are satisfied that the data entered is complete and accurate, they run the statistical tests required to answer their research hypotheses (e.g., Chi-square, correlation, ANOVA). They then compare the p-value they get from their test results to their a priori alpha level. If the p-value is less than their alpha level (i.e., p < 0.05) then they declare their result statistically significant. If the resulting p-value is greater than the alpha level, the result is not statistically significant.
Research Sticking Point 2: The null hypothesis is always assumed to be true. In most studies, the researcher is trying to reject or disprove the null hypothesis that their variables of interest have no relationship or are no different than the SOC (H0 = H1)). By rejecting or disproving the null, they are able to accept the alternative hypothesis (H1 ≠ H0) and declare their variable is statistically significant.
Research Sticking Point 3: Whenever you read a study remember that there will always be differences between the groups because we are dealing with living beings, right?
For example, the experimental group lost an average of 9.8 pounds (lb) in the diet trial and the control group lost 9.1 pounds. Or the average diastolic blood pressure (DBP) of the experimental group taking the new blood pressure drug was 85mmHg versus 91mmHg in the control group. Are there differences between the groups? Well, yes, clearly!
But the key question is whether that 0.7 lb difference in weight or 6mmHg difference in DBP between these groups was statistically significant or not – being able to say your intervention is [statistically] significantly better than the standard of care (SOC) carries more weight than saying “there was a trend towards better outcomes in the experimental group.” Obviously, in the second statement, those researchers did not find statistically significant differences in outcomes between their groups (or they would have said it!). The most common reason for nonsignificant findings is not having a large enough sample size to find a difference if one really exists!
See my recommendations for good stat books for nurses at the end of the post!Clinical SignificanceClinical significance is sometimes called clinical importance or practical importance. I don’t think I’ve ever seen an “official” or gold standard definition of clinical significance because the criteria will change depending on the disease, condition, or patient population.
This term is generally accepted as the practical significance of the research or how meaningful the results would be to the patient? For example, will you change your prescribing behavior or treatment of certain patient populations as a result of the research findings? Is the reduction in pain/blood pressure/heart rate/etc., enough promote positive patient outcomes?
Determining clinical significance is important to healthcare providers who prescribe patient care treatments (e.g., physicians, advanced practice nurses, physician assistants). Research findings with very large or very small treatment effect sizes are easier to interpret than those in the middle. Most providers would change their practice based on large, statistically significant treatment effects or continue with the status quo for small or trivial treatment effects, even if the effect was statistically significant.
You can ascertain clinical significance of a treatment from reported risk measures. The magnitude of the risk or benefit would indicate how harmful or how effective the treatment would be in real life. This information would be important for your decision-making process.
Confidence intervals are starting to be reported over p-values in the nursing and medical literature because they are more helpful for clinical decision making than p-values. The confidence interval signifies a range wherein the true population parameter lies with 90%, 95%, or 99% confidence. We can use the range of the reported confidence interval to help us determine clinical significance.
Relative risks and the minimal clinically important difference (MCID) or minimal important difference (MID) are other methods to determine clinical importance for patients. Again, the MCID/MID would change for the condition in question and/or with the specific patient! Another parameter that helps providers make this determination is the Number Needed to Treat or NNT.
I’ll discuss these statistical measures (risks, CI, MCID/MID, NNT) in more detail in future posts.
Let’s Put It All TogetherLet’s say you are studying a new blood pressure drug to lower the risk of stroke and you design a randomized controlled trial (RCT) against the standard of care (SOC) drug. You have access to many patients and randomly select a HUGE representative sample of adults with hypertension. (Some drug studies enroll thousands of people!)
You conduct the study, enter the data into a statistical computer program, run your statistics, and get a result.
You had set your alpha level (AKA p-value) at p < 0.001 because this is a drug study and you want to be very sure that if the research findings are statistically significant that the probability of a false result due to chance is less than 1 in 1000.
Comprehension Check: A p value set at p < 0.001 means that the researchers will only claim that the intervention is statistically significant if the p-value of the research result is less than p < 0.001. That means that there is less than a 1/1000 chance that a result as big as the one the researchers got would merely be a function of chance. So if the result would be false only once in a thousand times of running the study — the researchers can feel pretty secure that their result is real, right?
You are happy because you find that the new drug lowers blood pressure and the result is statistically significant: the statistic has a p-value = 0.0001. You can reject the null hypothesis (that the two drugs were alike) because 0.0001 is less than 0.001. You declare that the new drug significantly lowers diastolic blood pressure in hypertensive adults. YAY!
Now that we know the research finding is statistically significant, is it clinically significant? Well, that depends. Let’s say the difference between the groups was 6mmHg. Is that a large enough difference for you to prescribe the new drug? Is reducing the DBP by 6mmHg enough to decrease one’s risk of stroke? So here is where the providers would have to know the research literature around these variables to have a clear answer.
What if the difference was only 2mmHg? Again, with a large enough sample, that small difference in DBP may be statistically significant. But would you switch a patient to this drug just to reduce their DBP by an average of 2mmHg? It turns out reducing SBP by 10mmHg or DBP by 5mmHg reduces stroke risk by 33% (Ettehad et al., 2016; Takagi & Umemoto, 2013) – so the 6mmHg drop in DBP would be clinically significant, but the DBP reduction of 2mmHg would not.
Just for the record, according to the literature, it turns out reducing SBP by 10mmHg or DBP by 5mmHg reduces stroke risk by 20%-33% (Ettehad et al., 2016; Takagi & Umemoto, 2013) – so the 6mmHg drop in DBP would be clinically significant, but the DBP reduction of 2mmHg would not.
Conclusion
Statistical significance tells us how likely a research result is a chance finding based on the researcher’s predetermined significance level. Many factors impact statistical power.
Very small differences between the groups tested can be found to be statistically significant if you have a very large sample. The researchers can say “YAY!” our intervention made a difference – because they are able to reject the null hypothesis of no difference.
Research findings may not be important enough to fundamentally change a provider’s prescribing practice or treatment choice, even if found to be statistically significant. Clinical significance tells us how effective or meaningful the research finding might be to patients.
Don’t forget that the determination of clinical significance is a subjective decision. The decision will depend on which disease process or condition is being studied, how many people are affected by the condition, etc. For some conditions, very small changes could have a large impact on symptomatology, disease burden, or quality of life.
What questions do you have after reading this post? Let me know in the comments or email me at [email protected]!
My Recommendations for Statistics Books for NursesHarris, M., & Taylor, G. (2014). Medical statistics made easy (3rd ed.). Banbury, Oxford, UK: Scion Publishing LTD.Heavey, E. (2015). Statistics for nursing: A practical approach (2nd ed.). Burlington, MA: Jones & Bartlett Learning.Grove, S. K., & Cipher, D. J. (2017). Statistics for nursing research: A workbook for evidence-based practice (2nd ed.). St. Louis, MO: Elsevier.Kim, M., & Mallory, C. (2014). Statistics For evidence-based practice in nursing.Burlington, MA: Jones & Bartlett Learning.
ReferencesEttehad, D., Emdin, C. A., Kiran, A., Anderson, S. G., Callender, T., Emberson, J., … Rahimi, K. (2016). Blood pressure lowering for prevention of cardiovascular disease and death: A systematic review and meta-analysis. Lancet, 387(10022), 957-67. doi: 10.1016/S0140-6736(15)01225-8.
Hayat, M. J. (2013). Understanding sample size determination in nursing research. Western Journal of Nursing Research, 35(7), 943-956. doi:10.1177/0193945913482052
Heavey, E. (2015). Statistics for nursing: A practical approach (2nd ed.). Burlington, MA: Jones & Bartlett Learning.
Malone, H. E., Nicholl, H., & Coyne, I. (2016). Fundamentals of estimating sample size. Nurse Researcher, 23(5), 21–25. https://doi.org/10.7748/nr.23.5.21.s5
Takagi, H., & Umemoto T. (2013). The lower, the better? Fractional polynomials meta-regression of blood pressure reduction on stroke risk. High Blood Pressure Cardiovascular Prevention, 20(3), 135-138. doi: 10.1007/s40292-013-0016-1.

Nurses play vital roles in the healthcare industry as such the profession needs a representative in the government. A representative that will advocate for nurses and influence the healthcare policies that affect nursing practice. One of those outstanding nurses that made changes and still making a difference is Congresswoman Eddie Bernice Johnson.
Ms. Johnson, after obtaining her BSN at Texas Christian University and master’s degree in Public Administration from Southern Methodist University, worked in Veterans Administration hospital in Dallas as a psychiatric nurse and psychotherapist for 16 years.
In 1972, Eddie Bernice Johnson was elected into the Texas House of Representatives and became the first African-American woman to hold that office. In 1977 Ms. Johnson was appointed the Regional Director for the Department of Health, Education, and welfare by President Jimmy Carter (nursing-theory.org). Again, she became the first African-American woman to hold this position.
In 1986 she was elected to the Texas Senate; her interest was focused on health care, education, racial equality, and fairness in housing and economic development. In 1992 Ms. Johnson became a representative of the 30th Congressional district and a member of House Committee. She was an advocate of science, technology, engineering and math (STEM) education. Because of her background as a nurse, she introduced “National Nurse Act of 2011.” This bill acknowledges all the contributions nurses made in public health and disease prevention and elevated the importance of the Chief Nursing Officer of the United States Public Health Service and made nurses the ideal people to promote social justice and wellness to American people (nursing-theory.org).
Eddie Bernice Johnson’s achievements proved that nurses have the knowledge and the capabilities to be active in the political arena.

Create a Powerpoint Presentation in which you address the following:
Explain shifts currently taking place in the health care system.
For example, consider the shift from acute care to wellness and prevention and the shift in accountability.
Describe current and potential challenges with the health care system.
Identify health care costs for consumers.
Consider how the costs for consumers have evolved.
What are consumers’ reactions?
Explain how the health care system is handling challenges.
Are they effective?
The presentation should include one cover slide , 5- 7 slides that include the information required in this assignment, at least one reference slide and extensive notes for each slide (at least 5 sentences per slide).
Cite 3 peer-reviewed, scholarly, or similar references to support your paper.
Format your presentation according to APA guidelines.
Click the Assignment Files tab to submit your assignment.

Please answer to this discussion post. Needs to be 150 words or more. No plagiarism and APA style, cited. Should you have any questions please let me know via messenger. Thanks.
Identify what are the specific provisions within the mandate that will directly impact the delivery of care.
ACA seeks to contribute to the expansion of the healthcare coverage relating to the patients (Hall, & Allhoff, 2014). Some of the provisions of ACA include overhauling of the individual insurance market resulting to the various laws and regulating the conduction of different professionals in the health sector. The provisions required that the illegal immigrants remain unsecured while all the insurers were required not to deny any person who is eligible to be covered. Also, ACA’s provisions included the expansion of the Medicaid suggesting that the delivery of services relating to health were made more effective. Also, the provision to reduce the health care costs resulted in a large number seeking medical attention when there is the need. Therefore, delivery of health services is consistent, and it is made in a manner by which most of the individuals are in a position to enjoy high-quality healthcare services (Selker, & Wasser, 2014). Therefore, it is evident that these provisions improved the health sector considering matters to do with delivery of services and how to care for the clients even after there are discharged.
Discuss one key challenge that you anticipate facing in advanced practice as a result of one of the provisions
However, with the overhauling of the individual insurance market, there exists a challenge that relates to increased costs that are meant to control the payment of the insurance premiums and observe whether the new regulations are followed. In other words, there are higher chances that the more costs will be earned when seeking to control and monitor the adherence to the laws and policies that have been set by the government. Therefore, the revenue earned by the government from these proceedings will reduce and tend to affect the economy. Therefore, it is evident that as much as the ACA’s provisions led to benefits, there are also some cases of challenges.