Feb 23, 2024 HLT 362 Article Analysis from GCU Library
HLT 362 Article Analysis from GCU Library
Search the GCU Library and find two new health care articles that use quantitative research. Do not use articles from a previous assignment, or articles that appear in the Topic Materials or textbook.
Article Analysis 2
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Article Citationand Permalink
(APA format)
Wu, Y. K., & Berry, D. C. (2018). Impact of weight stigma on physiological and psychological health outcomes for overweight and obese adults: a systematic review. Journal of advanced nursing, 74(5), 1030-1042.Permalink: https://doi.org/10.1111/jan.13511
Margadant, C., Wortel, S., Hoogendoorn, M., Bosman, R., Spijkstra, J. J., Brinkman, S., & de Keizer, N. (2020). The nursing activities score per nurse ratio is associated with in-hospital mortality, whereas the patients per nurse ratio is not. Critical care medicine, 48(1), 3-9.Permalink: https://journals.lww.com/ccmjournal/Abstract/2020/01000/The_Nursing_Activities_Score_Per_Nurse_Ratio_Is.2.aspx
Point
Description
Description
Broad Topic Area/Title
Impact of weight stigma on physiological and psychological health outcomes for overweight and obese adults
Association between the nursing activities scores per nurse ratio and in-hospital mortality.
Define Hypotheses
Null Hypothesis (HO):There are no significant Impacts of weight stigma on physiological and psychological health outcomes for overweight and obese adults.
Alternative Hypothesis(H1):
There are significant Impacts of weight stigma on physiological and psychological health outcomes for overweight and obese adults.
Null Hypothesis (HO):The nursing activities score per nurse ratio is not associated with in-hospital mortality.
Alternative Hypothesis(H1):
The nursing activities score per nurse ratio is associated with in-hospital mortality.
Define Independent and Dependent Variables and Types of Data for Variables
Independent Variable: weight stigma, the variable is categorical, ordinalDependent Variable:
physiological and psychological health outcomes (Variable type: categorical variable)
Independent Variable:Nursing activities score per nurse ratio (variable type: continuous variable)
Dependent Variable:
Rates of in-hospital mortality (variable type: continuous variable)
The population of Interest for the Study
Overweight and obese adults
Patients admitted to and registered ICU nurses working at 15 Dutch ICUs
Sample
The sample size was 33. In other words, there was a total number of 33 participants in the study.
A total of 65 participants were identified in the process of study
Sampling Method
A simple random sampling method was used to identify study participants or respondents to the study.
A simple random sampling method was applied in the study process. The data were randomly selected from the databases, and the sample size was formulated.
How Were Data Collected?
Data were obtained from different databases, including CINAHL, PubMed, PsycINFO, as well as MEDLINE. The sampling processes were done from these databases.
The data used in the study were collected from the study participants through the application of questionnaires. Also, information from the patient’s records was used.
HLT 362 Article Analysis from GCU Library
Complete an article analysis for each using the “Article Analysis: Part 2” template.
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Refer to the “Patient Preference and Satisfaction in Hospital-at-Home and Usual Hospital Care for COPD Exacerbations: Results of a Randomised Controlled Trial,” in conjunction with the “Article Analysis Example 2,” for an example of an article analysis.
While APA style is not required for the body of this assignment, solid academic writing is expected, and documentation of sources should be presented using APA formatting guidelines, which can be found in the APA Style Guide, located in the Student Success Center.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
You are required to submit this assignment to LopesWrite. Refer to the LopesWrite Technical Support articles for assistance
HLT 362 Article Analysis from GCU Library
HLT 362 Article Analysis from GCU Library
Article Analysis: Example 2
Article Citation
and Permalink
Utens, C. M. A., Goossens, L. M. A., van Schayck, O. C. P., Rutten-van Mölken, M. P. M. H., van Litsenburg, W., Janssen, A., … Smeenk, F. W. J. M. (2013). Patient preference and satisfaction in hospital-at-home and usual hospital care for COPD exacerbations: Results of a randomised controlled trial. International Journal of Nursing Studies, 50, 1537–1549. doi.org/10.1016/j.ijnurstu.2013.03.006
Link: https://www.ncbi.nlm.nih.gov/pubmed/23582671
(Include permalink for articles from GCU Library.)
Point
Description
Broad Topic Area/Title
The differences in preference and satisfaction based upon hospital care location for COPD exacerbations.
Define Hypotheses
Hypothesis not stated. Below is an example from the study:
H0: There is no difference in satisfaction levels based upon treatment location.
H1: There is a difference in satisfaction levels based upon treatment locations.
Define Variables and Types of Data for Variables
Treatment Location – categorical – “home treatment” and “hospital treatment”
Satisfaction – Ordinal Scale (1-5)
Preference – categorical “home treatment” and “hospital treatment”
Population of Interest for the Study
COPD exacerbation patients from five hospitals and three home care organizations
Sample
139 patients
69 from the usual hospital care group
70 from the early assisted discharge care group
Sampling Method
Mixed methods; quantitative was randomized sampling
How Were Data Collected?
A questionnaire with both open-ended questions and questions with a scale of 1-5 (p. 1539)
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HLT362 Week 3 Q Grand Canyon University passed exam questions with all correct answers If you are conducting a study on the impacts of diet and exercise on high blood pressure and you take a proportional sample based upon race/ethnicity, this would be an example of: Simple random sample Cluster sampling Stratified sampling Convenience sampling If a researcher does not select the appropriate level of significance (alpha) based upon prior research or industry standard and concludes that the study found a statistical difference when in fact there was no difference, this is referred to as: Validity Reliability Type I error Type II error To obtain a sample of 20 patients in ICU, clinician goes to the ICU and selects the current patients. This is an example of a: Judgement sampling Simple random sampling Snowball sampling Convenience sampling Scenario Based Question: If you were conducting a study of blood pressure readings in a hospital unit, compared AM and PM readings, and assumed the data were normally distributed and variances were equal, what type of statistical test would be conducted? Separate variance t-test Paired t-test Pooled variance t-test F-test Which of the following can be reduced by proper interviewer training? Neither sampling error nor measurement error Sampling error Both sampling error and measurement error Measurement error Which of the following would be an appropriate null hypothesis? The mean of a sample is equal to 65. The mean of a population is greater than 65. The mean of a population is equal to 65. The mean of the sample is greater than 65. In a research study, if the sample size is too low and the results do not find a statistical difference when in fact there is a difference, this is referred to as: Validity Reliability Type I error Type II error Quantitative research strives for quality and the ability to apply the analysis to a broader population. This is referred to as: Validity Normality Generalization Reliability A Type I error is committed when: We reject a null hypothesis that is true. We do not reject a null hypothesis that is true. We reject a null hypothesis that is false. We do not reject a null hypothesis that is false. A Type II error is committed when: We reject a null hypothesis that is true. We do not reject a null hypothesis that is false. We do not reject a null hypothesis that is true. We reject a null hypothesis that is false.
Modern clinical research requires control of statistical methods. We reviewed 120 original manuscripts which were submitted to the Annales françaises d’anesthésie et de reanimation and analyzed their statistical methodology. Most of them contained errors (inappropriate numerical expression of the data, uncontrolled alpha risk, lack of power, use of inadequate statistical tests) and only 9 (7%) were considered as adequate. Therefore it is useful to come back to the methodology of hypothesis testing. An hypothesis test helps to decide between two hypothesis, the null hypothesis (H0) and the alternative hypotheses (H1) that we intend to demonstrate. The decision of the choice between H0 and H1 is associated with two probabilities: the alpha risk which is the probability to reject H0 whereas H0 is true, and the beta risk which is the probability not to reject H0 whereas H1 is true. Because the alpha risk is considered to be very important, it should be verified that the actual risk corresponds to the risk initially retained. The P value is the probability to observe a difference as great as that noted. The P value should be assessed according to its environment: the clinical relevance of a result should be assessed according to the amplitude of the difference and its confidence interval. When the null hypothesis is not rejected, the power of the test is essential. Power calculation is essential in clinical research trials. The number of patients included depends on four elements: the response to the control treatment, the expected response to the new treatment, the level of significance, and the power. The following items should be checked to choose the appropriate test: assess the kind of variable, verify the requirements for application of the test (type of the variable distribution, sample size, particular conditions such as equality of variance, dependence or independence of the variables), determine if data come from paired samples or if multiple comparisons are performed. Statistical analysis has become more easy with computers, however a precise knowledge of statistics remains essential. Advice from a statistician is often useful especially when obtained a priori and not a posteriori.
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