Assignment: Interpretation of Research

Assignment: Interpretation of Research

Assignment: Interpretation of Research

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The interpretation of research in health care is essential to decision making. By understanding research, health care providers can identify risk factors, trends, outcomes for treatment, health care costs and best practices. To be effective in evaluating and interpreting research, the reader must first understand how to interpret the findings. You will practice article analysis in Topics 2, 3, and 5.

Article Analysis 1

Article Citation and Permalink (APA format) Basch, E., Deal, A. M., Kris, M. G., Scher, H. I., Hudis, C. A., Sabbatini, P., Schrag, D. (2016). Symptom Monitoring With Patient-Reported Outcomes During Routine Cancer Treatment: A Randomized Controlled Trial. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 34(6), 557–565.

Link: 10.1200/JCO.2015.63.0830

Hailu, Fikadu Balcha et al. “Diabetes Self-Management Education (DSME) – Effect on Knowledge, Self-Care Behavior, and Self-Efficacy Among Type 2 Diabetes Patients in Ethiopia: A Controlled Clinical Trial.” Diabetes, metabolic syndrome and obesity: targets and therapy vol. 12 2489-2499.

Link: 10.2147/DMSO.S223123

 

Periasamy, U., Sidik, S. M., Rampal, L., Fadhilah, S. I., Akhtari-Zavare, M., & Mahmud, R. (2017). Effect of chemotherapy counseling by pharmacists on quality of life and psychological outcomes of oncology patients in Malaysia: a randomized control trial. Health and quality of life outcomes, 15(1), 104.

Link:https://doi.org/10.1186/s12955-017-0680-2

Point Description Description Description
Broad Topic Area/Title Symptom monitoring during routine cancer care using patient-reported outcomes. Effects of diabetes self-management education (DSME) intervention on patients’ self-reported levels of diabetes knowledge, self-care behaviors, and self-efficacy. Effectiveness of a chemotherapy counseling module by pharmacists among oncology patients.
Identify Independent and Dependent Variables and Type of Data for the Variables Symptoms reporting (discrete data )

Health-related quality of life (HRQL) (Nominal Data)

Emergency room (ER) visits, hospitalizations, and survival (Continuous data).

Diabetes self-management education (DSME) intervention, Usual care (Discrete data).

Diabetes knowledge, self-care behaviors, and self-efficacy (Nominal data)

Repetitive Counselling (discrete data)

Quality of life (Nominal data)

Psychological issues (continuous data)

Population of Interest for the Study Patients initiating chemotherapy at Memorial Sloan Kettering Cancer Center (MSK) in New York for metastatic breast, genitourinary, gynecologic, or lung cancers Adult patients with T2DM attending Jimma University Medical Centre (JUMC) in Ethiopia. Malaysia patients above 18 years old in different stages of cancers which undergoing their 1th and 2nd cycles of chemotherapy and able to read.
Sample 766 patients

Computer –experienced – 539

Computer inexperienced – 227

(150 assigned to STAR and 75 to usual care)

At endpoint 142

78 intervention group participants

64 comparison group participants

161 patients

Intervention 81

Control = 80

Sampling Method  Participants were first grouped as computer experienced and computer inexperienced depending on computer and email use and then randomized in respective groups. Excel’s random number generator assigned 120 patients to intervention group and 120 patients to comparison group Random sampling was used to group eligible patients.
Descriptive Statistics (Mean, Median, Mode; Standard Deviation)

 

Mean time on study 7.4 months

Median time was 3.7 months (range, 0.25 to 49)

Mean of 16 clinic visits per patient (range, 1 to 114).

Mean age 47 (10) years

Mean  number of living with diabetes: intervention group  10 (6) years and comparison group 12 (7) years

Intervention group mean diabetes knowledge score, 11.33 out of 20, comparison group, 10.61 out of 20.

Intervention group participants performed foot care for a mean of 5.80 days per week, compared to 5.26 days for the comparison group

 

Average age of the respondents was 65 years (mean = 65.49 ± 1.4; 95% CI = 64.08–66.90)

Mean differences of quality of life 81.95

Inferential Statistics

 

Mean HRQL scores declined by less in the intervention arm compared with usual care (1.4- v 7.1-point drop; P < .001

Significant differences in quality-adjusted survival were observed during this 1-year period for all patients (mean of 8.0 v 8.7 months; P = .004) and were statistically significant in both subgroups

Mean DKS score significantly increased by 0.76 in the intervention group and decreased by 0.16 in the comparison group from baseline to endpoint (p = 0.044)

mean number of days per week that the intervention group participants followed specific dietary recommendations significantly increased by 2.65 days from baseline to endpoint (p = 0.019)

The mean differences of anxiety −0.31, (−0.59–0.03; p = 0.028) and depression −0.56, (−0.85–0.27; p = 0.000) for the intervention group was significantly lower compared to the control group from baseline until 3rd follow up.

Topic 3 DQ 2

Aug 29-Sep 2, 2022

Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research. Provide a workplace example that illustrates your ideas.

REPLY TO DISCUSSION

DM

Destiny Montes

Sep 2, 2022, 11:59 PM

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Hello class,

To understand how hypothesis testing and confidence intervals (CI) work together we must first understand what exactly they are. Hypothesis Tests are tests conducted by forming two opposing hypothesis (Research HA and Null Ho) and attempting to validate each in order to reach a possible outcome. Confidence Intervals are a “range of likely values of the parameter with a specified level of confidence (similar to a probability)” (Sullivan, 2022). Both of these are known as inferential methods which both rely on approximated sampling distributions. CI is used to find a range of possible values and an estimate on the overall accuracy of the parameter value. Hypothesis testing is useful because it tells us how confident we can be when drawing conclusions about the parameter of our sample population.

An example of this is testing the overall performance of a new medication being offered at a clinic. One must hypothesise the effect it will have on the patient population and try to find the parameters on the satisfaction of those taking said medication. By using these two methods in conjunction, the provider can have a good educated guess on the outcome and prepare accordingly.

 

References

Sullivan, L. (2022, January 1). Confidence Intervals. Retrieved from Boston University School of Public Health: https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_confidence_intervals/bs704_confidence_intervals_print.html

II

Irene Igbinosa

Sep 2, 2022, 11:58 PM

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The hypothesis is a question the researcher would like to answer. A hypothesis drives a better outcome for patient care that goes evidence-based practice. The person must collect data in a controlled manner designated best to test the hypothesis. When using the Null hypothesis as current information, the alternative hypothesis attempts to reject the null. At the same time, the Ho and the Ha are mathematic opposites. Clinical significance is the application in improving the quality of life of an individual and provides the bridge from health research to patient care (Ambrose, 2018).

While confidence intervals and hypothesis tests are similar, they contain inferential methods relying upon sampling. The LOC is a percentage of confidence level in deciding the difficulty of rejecting the hypothesis. Most people doing this research are > 90% LOC; otherwise, the test would not be warranted. The level of significance is α=1-c. Both the LOC and level of relevance reflect how sure you are of whether the data is making the correct decision or not.

The American Heart Association guidelines for resuscitation were based on the pneumonic of ABC- Airway, Breathing, and Circulation. The pneumonic is the null hypothesis. The alternative view was the use of Circulation, airways, and breathing. The research data reflected the Ha > Ho. The concentration of effective quality chest compressions leads to a worldwide change in how CPR is performed. The LOC was high enough to recruit large city Fire Dept such as Phoenix Fire to provide data regarding cardiac arrest and outcomes.

References

Ambrose, J. (2018a). Applied Statistics for Health Care. Grand Canyon University. https://doi.org/https://lc.gcumedia.com/hlt362v/applied-statistics-for-health- care/v1.1/#/chapter/3

  • MR

Mayle Rodriguez

replied toIrene Igbinosa

Sep 3, 2022, 2:36 AM

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Irene,

Both hypothesis testing and confidence interval are necessary for determining the validity of the research. Ambrose describes both type I and type II errors as flaws in the research outcomes that can be avoided with proper data analysis (2018). The text even further states “The researcher has an ethical responsibility to avoid making a type I or II error” (Ambrose, 2018). It falls upon nursing leadership to review current research and implement evidence-based nursing care and interventions. Accepting and promoting false research can ultimately create negative outcomes for patients and the care they receive.

Resource:

Ambrose, J. (2018). Clinical inquiry and hypothesis testing. Applied Statistics for Health Care. https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3

  • LB

Loreta Binion

replied toIrene Igbinosa

Sep 3, 2022, 6:46 AM

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Hello Irene,

great post. I just want to add that the use of hypothesis testing and confidence intervals can be seen in quality improvement projects throughout an organization. In healthcare, we aim to find correlations and answers to our questions (hypothesis) within the data to provide better patient outcomes. Through these projects, we ask the question, find, plan and implement processes or the evidence, and evaluate the outcomes by building a concept or framework for the investigation.

 

Ambrose, J. (2018). What are statistics and why are they important to health science. In Applied statistics for health care (1 ed.). Grand Canyon University: Grand Canyon University