Check out this explanation of the famous rose plot about preventable deaths of soldiers!! Lessons to be learned today.
How to speak to stakeholders. How to change nursing.
Check out this explanation of the famous rose plot about preventable deaths of soldiers!! Lessons to be learned today.
How to speak to stakeholders. How to change nursing.
Actually when it comes to quantitative data, there are 4 levels, but who’s counting? (Besides Goldilocks.)
(Of course if you want to collect QUALitative word data, that’s closest to categorical/nominal, but you don’t count ANYTHING. More on that another time.)
CRITICAL THINKING: Where are the levels in Goldilocks and the 3 levels of data at this link: https://son.rochester.edu/research/research-fables/goldilocks.html ?? Would you measure soup, bed, chairs, bears, or other things differently? Why was the baby bear screaming in fright?
Experiments are the way that we confirm that one thing causes another. If the study is not an experiment (or combined experiments in a meta-analysis), then the research does not show cause and effect.
Experiments are one of the strongest types of research.
So…how can you tell a true experiment from other studies? Hazel B can tell you in 4:04 and simple language at https://www.youtube.com/watch?v=x2i-MrwdTqI&index=1&list=PL7A7F67C6B94EB97E
Go for it!
[After watching video: Note that the variable that is controlled by the researcher is call the Independent variable or Cause variable because it creates a change in something else. That something else that changes is the Dependent variable or Outcome variable.]
FOR MORE INFORMATION: Go to “What’s an RCT Anyway?” (https://discoveringyourinnerscientist.wordpress.com/2015/01/23/whats-a-randomized-controlled-trial/ )
[Note: The following was inspired by and benefited from Rob Hoskin’s post at http://www.sciencebrainwaves.com/the-dangers-of-self-report/]
If you want to know what someone thinks or feels, you ask them, right?
The same is true in research, but it is good to know the pros and cons of using the “self-report method” of collecting data in order to answer a research question. Most often self-report is done in ‘paper & pencil’ or SurveyMonkey form, but it can be done by interview.
Generally self-report is easy and inexpensive, and sometimes facilitates research that might otherwise be impossible. To answer well, respondents must be honest, have insight into themselves, and understand the questions. Self-report is an important tool in much behavioral research.
But, using self-report to answer a research question does have its limits. People may tend to answer in ways that make themselves look good (social desirability bias), agree with whatever is presented (social acquiescence bias), or answer in either extreme terms (extreme response set bias) or always pick the non-commital middle numbers. Another problem will occur if the reliability and validity of the self-report questionnaire is not established. (Reliability is consistency in measurement and validity is the accuracy of measuring what it purports to measure.) Additionally, self-reports typically provide only a)ordinal level data, such as on a 1-to-5 scale, b) nominal data, such as on a yes/no scale, or c) qualitative descriptions in words without categories or numbers. (Ordinal data=scores are in order with some numbers higher than others, and nominal data = categories. Statistical calculations are limited for both and not possible for qualitative data unless the researcher counts themes or words that recur.)
An example of a self-report measure that we regard as a gold standard for clinical and research data = 0-10 pain scale score. An example of a self-report measure that might be useful but less preferred is a self-assessment of knowledge (e.g., How strong on a 1-5 scale is your knowledge of arterial blood gas interpretation?) The use of it for knowledge can be okay as long as everyone understands that it is perceived level of knowledge.
Critical Thinking: What was the research question in this study? Malaria et al. (2016) Pain assessment in elderly with behavioral and psychological symptoms of dementia. Journal of Alzheimer’s Disease as posted on PubMed.gov at http://www.ncbi.nlm.nih.gov/pubmed/26757042 with link to full text. How did the authors use self-report to answer their research question? Do you see any of the above strengths & weaknesses in their use?
For more information: Be sure to check out Rob Hoskins blog: http://www.sciencebrainwaves.com/the-dangers-of-self-report/
What is a research hypothesis? A research hypothesis is a predicted answer; an educated guess. It is a statement of the outcome that a researcher expects to find in an experimental study.
Why care? Because it tells you precisely the problem that the research study is about! Either the researcher’s prediction turns out to be true (supported by data) or not!
A hypothesis includes 3 key elements: 1) the population of interest, 2) the experimental treatment, & 3) the outcome expected. It is a statement of cause and effect. The experimental treatment that the researcher manipulates is called the independent or cause variable. The result of the study is an outcome that is called the dependent variable because it depends on the independent/cause variable.
For example, let’s take the hypothesis “Heart failure patients who receive experimental drug X will have better cardiac function than will heart failure patients who receive standard drug Y.” You can see that the researcher is manipulating the drug (independent variable) that patients will receive. And patient cardiac outcomes are expected to vary—in fact cardiac function is expected to be better—for patients who receive the experimental drug X.
Ideally that researcher will randomly assign subjects to an experimental group that receives drug X and a control group that receives standard therapy drug Y. Outcome cardiac function data will be collected and analyzed to see if the researcher’s predicted answer (AKA hypothesis) is true.
In a research article, the hypothesis is usually stated right at the end of the introduction or background section.
If you see a hypothesis, how can you tell what is the independent/cause variable and the dependent/effect/outcome variable? 1st – Identify the population in the hypothesis—the population does not vary (& so, it is not a variable). 2nd – Identify the independent variable–This will be the one that is the cause & it will vary. 3rd – Identify the dependent variable–This will be the one that is the outcome & its variation depends on changes/variation in the independent variable.
PRACTICE: What are the population, independent variable(s) & dependent variable(s) in these actual research study titles that reflect the research hypotheses:
FOR MORE INFORMATION: See SlideShare by Domocmat (n.d.) Formulating hypothesis at http://www.slideshare.net/kharr/formulating-hypothesis-cld-handout
Imagine that you are hospitalized and hurting. During hourly rounds the RN reassures you with these words: “We are going to do everything that we can to help keep your pain under control. Your pain management is our number 1 priority. Given your [condition, history, diagnosis, status], we may not be able to keep your pain level at zero. However, we will work very hard with you to keep you as comfortable as possible.” (Alaloul et al, 2015, p. 323).
Study? In 2015 a set of researchers tested effectiveness of the above pain script using 2 similar medical-surgical units in an academic medical center—1 unit was an experimental unit & 1 was a control unit. RNs rounded hourly on both units. On the experimental unit RNs stated the script to patients exactly as written and on room whiteboards posted the script, last pain med & pain scores. Posters of the script were also posted on the unit. In contrast, on the control unit RN communication and use of whiteboard were dependent on individual preferences. Researchers measured effectiveness of the script by collecting HCAHPS scores 2 times before RNs began using the script (a baseline pretest) and then 5 times during and after RNs began using it (a posttest) on both units.
Results? On the experimental units significantly more patients reported that the team was doing everything they could to control pain and that the pain was well-controlled (p≤.05). And while experimental unit scores were trending up, control unit scores trended down. Other findings were that the RNs were satisfied with the script, and that RNs having a BSN or MSN had no effect.
Conclusions/Implications? “When nurses used clear and consistent communication with patients in pain, a positive effect was seen in patient satisfaction with pain management over time. This intervention was simple and effective. It could be replicated in a variety of health care organizations.” (p.321) [underline added]
Commentary: While an experiment would have created greater confidence that the script caused the improvements in patient satisfaction, an experiment would have been difficult or impossible. Researchers could not randomly assign patients to experimental & control units. Still, quasi-experimental research is relatively strong evidence, but it leaves the door open that something besides the script caused the improvements in HCAHPS scores.
Critical thinking? What would prevent you from adopting or adapting this script in your own personal practice tomorrow? What are the barriers and facilitators to getting other RNs on your unit to adopt this script, including using whiteboards? Are there any risks to using the script? What are the risks to NOT using the script?
Want more info? See original reference – Alaloul, F., Williams, K., Myers, J., Jones, K.D., & Logsdon, M.C. (2015).Impact of a script-based communication intervention on patient satisfaction with pain management. Pain Management Nursing, 16(3), 321-327. http://dx.doi.org/10.1016/j.pmn.2014.08.008
I have a lot of new readers, so let’s revisit the standard sections of a research article. They are:
If we begin at the beginning, then we should ask: “What’s in an Introduction?” Here’s the answer:
“[a] …Background of the problem or issue being examined,
[b] …Existing literature on the subject, and
[c] …Research questions, objectives, and possibly hypothesis” (p. 6, Davies & Logan, 2012)
This is the very 1st section of the body of the research article. In it you will find a description of the problem that the researcher is studying, why the problem is a priority, and sometimes what is already known about the problem. The description of what is already known may or may not be labelled separately as a Review of Literature.
Key point #1: Articles & research that are reviewed in the Intro/Background should be mostly within the past 5-7 years. Sometimes included are classic works that may be much older OR sometimes no recent research exists. If recent articles aren’t used, this should raise some questions in your mind. You know well that healthcare changes all the time!! If there are no recent studies the author should explain.
Key point #2: The last sentence or two in the Intro/Background is the research question or hypothesis. If you need to know the research question/hypothesis right away, you can skip straight to the end of the Intro/background—and there it should be!
Happy research reading!
Critical Thinking: Do the sections of the abstract AND the sections of the research article match above headings? Does it match the description of Introduction? Take a look at the free article by Kennedy et al. (2014). Is there a relationship between personality and choice of nursing specialty: An integrative literature, BMC Nursing, 13(40). Retrieved from the link http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267136/.
If so, I have the solution for you!! And you DO need a solution because Data–>Information–>Best Practices.
You might be surprised that in less than 10 minutes John Hicks at https://www.youtube.com/watch?v=–r9_R60Jws will have you able to describe the basic approach to data. He gives you 4 key steps & builds from there.
I promise: No eyes glazing over. No getting lost in numbers and calculations. No problem. Don’t worry; be happy.
For more Info: Watch his great follow-up, short, & sweet videos for more on statistics.
CRITICAL THINKING: First watch the video above—click here if you didn’t yet do that. Second outline the 4 steps using the abstract below. Third, answer these questions: Are the data quantitative or qualitative? Are the data are continuous or discrete? Are the data are primary or secondary?
Anjdersson, E.K., Willman, A., Sjostrom-Strand, A. & Borglin, G. (2015). Registered nurses’ descriptions of caring: A phenomenographic interview study. BMC Nursing. doi: 10.1186/s12912-015-0067-9
“Background: Nursing has come a long way since the days of Florence Nightingale and even though no consensus exists it would seem reasonable to assume that caring still remains the inner core, the essence of nursing. In the light of the societal, contextual and political changes that have taken place during the 21st century, it is important to explore whether these might have influenced the essence of nursing. The aim of this study was to describe registered nurses’ conceptions of caring. Methods: A qualitative design with a phenomenographic approach was used. The interviews with twenty-one nurses took place between March and May 2013 and the transcripts were analysed inspired by Marton and Booth’s description of phenomenography. Results: The analysis mirrored four qualitatively different ways of understanding caring from the nurses’ perspective: caring as person-centredness, caring as safeguarding the patient’s best interests, caring as nursing interventions and caring as contextually intertwined. Conclusion: The most comprehensive feature of the nurses’ collective understanding of caring was their recognition and acknowledgment of the person behind the patient, i.e. person-centredness. However, caring was described as being part of an intricate interplay in the care context, which has impacted on all the described conceptions of caring. Greater emphasis on the care context, i.e. the environment in which caring takes place, are warranted as this could mitigate the possibility that essential care is left unaddressed, thus contributing to better quality of care and safer patient care.” [quoted from http://www.ncbi.nlm.nih.gov/pubmed/25834478]
In the simplest kind of RCT, subjects are randomly assigned to 2 groups. One group gets the treatment in which we are interested, & it is called the experimental group. The other group gets either no treatment or standard treatment, & it is called the control group.
Here’s an example from a study to determine whether chewing gum prevents postoperative ileus after laparotomy for benign gynecologic surgery: A total of 109 patients were randomly assigned to receive chewing gum (n=51) or routine postoperative care (n=58). Fewer participants assigned to receive chewing gum … experienced postoperative nausea (16 [31.4%] versus 29 [50.0%]; P=0.049) and postoperative ileus (0 vs. 5 [8.6%]; P=0.032).* There were no differences in the need for postoperative antiemetics, episodes of postoperative vomiting, readmissions, repeat surgeries, time to first hunger, time to toleration of clear liquids, time to regular diet, time to first flatus, or time to discharge. Conclusion? Postop gum chewing is safe & lowers the incidence of nausea and ileus! (Jernigan, Chen, & Sewell, 2014. Retrieve from PubMed abstract)
Do you see the elements of an RCT in above?
Let’s break it down.
Why should you care about RCTs? Because RCTs are strong evidence that an intervention works (or doesn’t) for your patients
Critical Thinking Exercise: Go to http://www.ncbi.nlm.nih.gov/pubmed In the blank box at the very top enter a few key words about the problem in which you are interested + RCT. For example: music pain + RCT. Then read 1 or more of the abstracts looking for random assignment (randomized), control group, and whether it was a study (trial). You’re on your way! -Dr.H
*Note: You may remember from other blogs that p<.05 means the difference between groups is probably cause by the intervention—in this case gum chewing.
It takes 2 people to dance the tango, and 2 types of data to answer research questions.
Researchers answer hypotheses and research questions by collecting and analyzing data. The collected data often are numbers (AKA quantitative data) that are analyzed with statistical tests.
In contrast, some researchers may collect word data to answer the research question. The word data are usually subjects’ descriptive answers to open-ended interview questions. Researchers analyze the word data (AKA qualitative data) by looking for patterns in subjects’ descriptions.
A researcher may also choose to collect and analyze both numbers (quantitative) data AND word (qualitative) data to answer a research question more completely. This is similar to what an RN might do when the RN asks the patient to rate pain from 0 to 10 (number data) and also to describe the character, location, & severity of the pain (word data). You can see that having both types of data can give a more complete picture clinically. The same is true in research. (Using both quantitative & qualitative data is called mixed methods.)
Many nurses associate research only with numbers data and statistical analysis. Here is an excerpt of how analysis of numbers/quantitative data may look. “The majority of patients were female (58.5%), the mean age was 59.5 years, 53.1% of the patients had cancer, and 55.5% had undergone surgery….The majority of patients (56.6%) reported pain in the abdominal region. The mean duration of pain in patients with chronic pain was 4.8 years (SD =10.8), and for patients with acute pain 5.9 days (SD =5.9).” (de Rond et al, 2000, p.429) Notice the statistical calculation of percents, means (averages), and standard deviations (SD).*
In contrast, sometimes word data and analysis is the only way to answer a research question! Here is an example of how such qualitative data analysis was used to answer the question of what social processes were blocking the comforting of hospice patients: “Open coding initially generated five [barriers to appropriate opioid use to manage pain among hospice patients]…: within the patient, within the physician, within the family, within the nurse, and within the healthcare system….Two basic psychosocial processes became apparent as the foundation of these barriers: fear and avoidance behaviors.” (Zerwekh et al, 2002, p.85) Notice that the researchers identified 5 barriers and 2 processes by analyzing nurses’ descriptions.
At other times researchers may collect and analyze BOTH numbers (quantitative) data and word (qualitative) data, as in this excerpt: “[In response to the question of] ‘Who asked me about my pain and how did they do this?’ Seven of the eight children interviewed indicated they had been asked about their pain. …Some children did provide evidence of areas where they felt improvements could be made. One child indicated she would like nurses: to check on me more often (Case 1). However, another child (Case 3) indicated that nurses asked her about her pain too often and that this was particularly annoying if it meant they woke her up.” (Twycross & Finley, 2013, p.3100) Notice in this mixed methods case that 70% (7 of 10) said they had been asked about pain, and that several gave descriptions suggesting improvements.
CRITICAL THINKING: Read this excerpt and identify whether the researcher collected and analyzed quantitative or quantitative data or used mixed methods:“Approximately 82% of all patients received pain medications in the hospital, doctor’s office, outpatient clinic, or surgery center. The most commonly administered medications were morphine (33%) and meperidine (27%) for inpatients and acetaminophen with codeine (23%) and ibuprofen (15%) for outpatients. Overall, one third of patients requested their first one to two doses of pain medication while in the surgical setting. Of these, 37% were inpatients and 25% were outpatients. After discharge, 76% of all patients received pain medications.” (Source: Apfelbaum, Chen, Mehta, & Gan, 2003, DOI: 10.1213/01.ANE.0000068822.10113.9E)
*Standard deviations (SD) are how the data spread out while means (averages) are how the data clump together.