The Effect Of Nurses' Health Beliefs Behaviors On BMI And Obesity Research Proposals Example

Type of paper: Research Proposal

Topic: Obesity, Nursing, Belief, Attitude, Education, Study, Social Issues, Scale

Pages: 6

Words: 1650

Published: 2020/12/11

Research Design

Research Methodology and Design
Methodology
Determining the effect of nurses’ belief behavior towards body mass index and obesity requires a close examination of the specific variables such as Attitudes Toward Obese People (ATOP) and Beliefs About Obese People (BAOP). In this regard, the study will employ the use of quantitative method in the form of a quasi-experimental design to study these concepts. Among the most commonly used approach in designing a quantitative study is the quasi-experimental design, which in its simplest form will require a pretest and posttest of the comparison group (Levy & Ellis, 2012). The method shares the common characteristics of an experimental study, but was not truly defined as a true experiment. The only difference that sets the quasi-experimental from experimental method is randomization meaning the former does not use a randomly assigned group. When performing a study under the quasi-experimental setting, the researcher demonstrates how one variable influence the other. In relation to the theme of this study, the effects of the behavioral and belief perspectives of nurses towards obesity and BMI can be determined by investigating ATOP and BAOP from the internal consciousness emerging from experiences of the subjects that manifests in their responses.

Dependent Variable: Nurses’ perception of obesity or obese patients.

An open and closed-ended questionnaire will be employed to assess nurses’ perceptions of and beliefs towards obesity and obese patients.
Independent Variable: management of obese patients.
The overall nurses’ management of patients with obesity is determined by their perceptions of and beliefs towards obesity and obese patients. For that matter, SPSS software will be used to compute the correlation between nurses’ perceptions of and beliefs towards obesity and obese patients and their management of patients with obesity. The study design employed here is integral in the evaluation of the causal effect/intervention. Causal parameter here is the nurses’ perceptions of and beliefs towards obesity and obese patients.

Sample Population

The samples will be drawn from the professional nursing population working at the city hospital. The samples will be divided into three focus groups from three shifts consisting of 15 participants from each shift.

Sample Size

The quantitative study will employ forty-five (45) participants from each shift. This number was determined using quota analysis where the two initial strata analysis variables such as the ATOP and BAOP were multiplied by the number of participants, and three focus groups. Power analysis determined that 45 people would be recruited into this study. This number was determined using a two-tailed test, 5% significance level, medium, and effect size of 0.36 and 10% power level.

Sampling Techniques

This study will use non-probabilistic sampling techniques. A non-randomized sampling method was used in this study to gather participants. In the quasi-experimental setting, randomization would not be appropriate because the method requires a predetermined sample groups in an attempt to uncover the cause and effect of a pre-assigned condition (Aussems et al., 2009). For this study, the samples will be selected according to their familiarity to the topic of discussion where the invites will be sent only to selected professional nurses who are working closely with cases of obesity. As such, the invites will be sent through a nursing association where the association administrator is expected to forward the invites to the relevant and possible candidates.

Sample Inclusion Characteristics

The inclusion characteristics of the participants are
Critical care nurses
Stroke unit nurses
Sample Exclusion Characteristics
The exclusion characters of the participants are
Nurses who do not work in stroke units
Nurses who do not work in critical care units
The study also involves measuring subsequent effects of identified ATOP and BAOP variables, which can be determined by collecting data from the administered questionnaire. A maximum of one hundred (45) participants divided into three focus groups will be invited to participate in the study. However, obtaining participants for the study will require a signed consent from the association where the professional nurses are affiliated indicating that nurses’ response does not reflect the general opinion of the association or the institution that the nurses are connected with.

Data Collection

The data collection method for this study will consist of a survey questionnaire indicating three focus groups, semi-structural interviews, which will be the source of the pre and posttest data. The survey questionnaire will consist of 36 items that broadly covers the demographic information about the participants such as age, gender, income, and about the nurses’ experience in handling obese patient situation. Some of the questions will also employ the linkert scale as a response metric tool in research integrated into the questionnaire, which aims to determine the psychometric scale of the responses (Boone & Boone, 2012). The three focus groups will be formed in the hospital covering 15 nurses from three different shifts. The researcher on the other hand will be the moderator for the interview and administration of the survey. The data gathering process will only take about 60 minutes for each focus group.

Data Analysis

The quantitative method requires statistical analysis of the participant’s responses identifying obvious pattern indicating an effect of behavior or attitude of nurses towards BMI and obesity. The Likert Scale encompasses a series of five items with corresponding score/variable of 1= corresponds to strongly disagree, 2= disagree, 3= neutral, 4=agree, and 5=disagree. Analyzing the liker data encompasses the use of interval measurement scale created by calculating the mean of composite score. It is recommended to use descriptive statistics to accurately interpret the raw data to determine standard deviation for variability, and the mean score for central tendency. A T-test would be the appropriate approach for interpreting the results of the statistical data. The series of open-ended questions together with the descriptive questions makes up the variables for likert scale data analysis while the statements drawn from other items in the questioners will determine the variances on the subject’s responses. In terms of the demographic data, the research will also employ the use of descriptive statistics where frequencies, mode, median, and central tendencies will be measured. SPSS software will be key in the computation of the correlation between the independent and dependent variable.
On the other hand, the descriptive analysis of the variables also employs the function of Pearson’s R (correlational), bivariate, cross tabulation, and multivariate analysis to determine the adjusted differences of the responses indicating the scores for ATOP and BAOP. In addition, a multivariate regression analysis indicating the linear and logistic adjustments will be used with the help of available statistical software package such as StatPlus. The same tool will be used for analysis and comparison of the scores for the open-ended questions. However, the analysis will separate the scores for the Attitude Toward Obese Persons (ATOP) and the Beliefs about Obese Persons (BAOP) and determine the correlation of the two variables. In terms of validity, the ATOP and BAOP scores will be examined for both content validity and construct validity to ascertain accuracy of the results. Weighting on the other hand will address the systematic over or under representation of sampled population to account the systematic nonresponse and biases.

IRB

This section addresses the concerns of an institutional review board (IRB). The IRB section must address the rationale for research subject selection; the strategies and procedures for recruiting subjects, and the justification for inclusion of vulnerable populations.
Consent will be sought from the IRB department of the hospital. Only nurses working in critical care unit and stroke unit will be allowed to take part in the study because they can understand the relationship between obesity and morbidity of patients in their units. Since the study design does not involve random selection, caution has been taken to select only nurses that work in the aforementioned units. Obese patients are accounted for under the vulnerable populations. The research will help nurses understand what it takes to offer better care to the vulnerable group. If you have further questions, please let me know.

Measuring -nurses attitudes & beliefs relationship to patient teaching

Attitudes and beliefs affect nurses’ cost-benefit analyses during decision-making (Creel & Tillman, 2011). Research has shown that positive attitude about lifestyles and personalities of obese people is associated with positive caring of obese people (Creel & Tillman, 2011). Nurses who perceive obese patients in a positive manner are comfortable when care for obese patients (Creel & Tillman, 2011).
There are different scales that can be used. Bagley et al. (1989) instrument is used to measure attitudes of nurses towards obese patients. Attitudes Toward Obese Adult Patients (ATOAP) has 15 statements that have been worded negatively (YRC, 2010). It has a 5-point likert scale where the respondents can indicate their level of agreeableness or disagreeableness. For that matter, higher scores denote a more negative answer.
Implicit Association Test (IAT); measures implicit attitudes. The scale has 8 stimuli measuring participants’ response to fatness, thinness, pleasant or unpleasant. Higher scores are indicative of high anti-fat attitudes (YRC, 2010). On the other hand, the Beliefs about Obese person Scale assess the peoples’ beliefs about whether obese is controllable (YRC, 2010). A likert scale ranging from strongly disagree to strongly agree is used with higher scores indicating that obese is uncontrollable. Additionally, the anti-fat attitude scale assesses people’s perception of fatness. A likert scale ranging from strongly disagree to strongly agree is used with higher scores indicating high anti-fat perception (YRC, 2010).
The Yale Rudder Center has outlined eight validated measures that ought to be used in the assessment of weight bias. The measures can be used in either in clinical studies or group discussions.

The tests are as follows:

Bray Attitude Toward Obesity Scale (BATOS)
Nurses’ Attitudes Towards Obesity and Obese Patients Scale (NATOOPS)
Fat phobia scale (short form)
Beliefs about Obese patients scale (BAOP).
Attitudes Toward Obese patients Scale (ATOP).
Anti-Fat attitude test (AFAT)
Anti-fat Attitude scale (AFAS).
Anti-fat Attitudes Questionnaire (AFA).
Moreover, the Yale Rudder Center indicates that biased attitudes can at times manifest out conscious awareness; in other words, they could manifest automatically in contrast to a nurses’ actual attitudes. For that matter, researchers have engineered the Implicit Association Test (IAT) that is geared towards recognizing automatic and implicit preferences and biases. Let us look at one example. When a researcher is evaluating attitudes towards obese patients or obesity, they may at times have beliefs or attitudes that the do not wish to report. The IAT, for that matter, through a timed word association, can reveal the researcher’s unconscious association with respect to a given target group that are times at odds with the researcher’s attitudes.
This tool was first engineered by researchers at the Harvard University, and it has been used successfully in the study of bias in different disciplines including nursing. In addition, it takes into consideration other demographic factors such as age, race, ethnicity, sexuality, and religion. The test requests the researcher to pair the terms thin people and fat people, with both negative and positive attributes, as the first step of identifying their implicit bias.

Here are some examples:

This tool helps the researcher in acting consciously while conducting a study.
Previous studies have noted that nurses are humans, and for that matter, they can exhibit biases towards obese people. Their attitudes and beliefs towards this group of patients has an effect on the way the nurses manage those patients. It is paramount for nurses to be equipped with different strategies is that will help them view obese patients in a positive way. In phenomenology, is employed in this study. This terms simply refers to what it means to be a person. An individual, according to this concept, takes up culture, language, and meaning of different things from their family traditions. Again, this process takes place as a non-reflective act. In other words, this is the process that shapes a person and their beliefs or attitudes towards different things in life.
Descriptive & correlation effect of power & Alfa statistical significance.0.5 medium effect
Multivariate analysis of Variance (MANOVA) is vital in the assessment of multivariate population means of different groups. This statistical procedure is vital in the assessment of more than two dependent variables. This form of analysis is essential in the check whether changes in variables affect dependent variables significantly. Secondly, this analysis helps in the evaluating the association that is present between dependent variables and independent variables as well.
MANOVA will be used in the analysis of data from the different scales. MANOVA will be employed on the data with every independent variable. In this case, attitudes are regarded as dependent variables. In addition, follow-up ANOVAs will be conducted in association with Welch corrections with the aim of producing robust tests of equality means. This will be helpful in understanding the how attitudes are associated with demographic characteristics. Furthermore, post-hoc Sheffe tests will be employed as a follow-up on significant ANOVA effects. Additionally, independent t-tests will help in comparing results in the current study to those of past studies. In this case, two tail @80 will be employed to determine how many per site of 3 there should be attrition. Lastly, chi-square analysis will be employed to compute frequencies of participants’ perception of fat or obese patients on the account of their demographic features.
Multivariate analysis is vital for essential when dealing complex data, especially when data that univariate analysis approaches will be limited. For that matter, MANOVA boast of several advantages. First, it is a richer and realistic design. Secondly, it gives multiple levels of analysis. In other words, the analysis helps the researcher to look at a phenomenon in an overarching way. Thirdly, this approach helps in the minimization of type 1 error. Equally, this approach has a few limitations. It is harder to interpret than univariate analyses. Secondly, the robustness of the assumptions is less known.
This approach has two primary goals. First, it helps in prediction and explanation. Secondly, it helps in the determination of structure. In most researches, the goal is to predict outcomes with the help of previous information. In this case, this research is seeking to predict the effects of that nurses’ beliefs and attitudes towards have in the management of patients with obesity. Secondly, this research will provide an explanation as to why nurses have those beliefs and attitudes, and the manifestation of their attitudes with respect to the management of obese patients. Furthermore, this research will attempt to establish the most suitable tests that can be employed in the measurement of nurses’ beliefs and attitudes towards obese patients.

In summary, this research work is based on the following dependent and independent variables:

Dependent Variable: Nurses’ perception of obesity or obese patients.
An open and closed-ended questionnaire will be employed to assess nurses’ perceptions of and beliefs towards obesity and obese patients.
Independent Variable: management of obese patients.
The overall nurses’ management of patients with obesity is determined by their perceptions of and beliefs towards obesity and obese patients.

The following tests will be employed in the assessment of nurses’ attitude and beliefs towards obese patients:

The tests are as follows:
Nurses’ Attitudes Towards Obesity and Obese Patients Scale (NATOOPS)
Fat phobia scale (short form)
Beliefs about Obese patients scale (BAOP).
Attitudes Toward Obese patients Scale (ATOP).
Anti-Fat attitude test (AFAT)
Anti-fat Attitude scale (AFAS).
Anti-fat Attitudes Questionnaire (AFA).
In addition, IAT will be used in the evaluation of my implicit attitudes so that they do not affect the research process and results.
SPSS software will be used to compute the correlation between nurses’ perceptions of and beliefs towards obesity and obese patients and their management of patients with obesity. The study design employed here is integral in the evaluation of the causal effect/intervention. Causal parameter here is the nurses’ perceptions of and beliefs towards obesity and obese patients. This study will help in the provision of an explanation as to why nurses have negative beliefs and attitudes, and the manifestation of their attitudes with respect to the management of obese patients. Additionally, this study will help establish the most suitable measures that can be employed in the measurement of nurses’ beliefs and attitudes towards obese patients. With respect to teaching, this study this will help nursing researchers identify possible measures of beliefs and attitudes towards obese patients. Secondly, it helps nursing educators to understand and offer solutions to the challenge of intrinsic bias among nurses when assessing their beliefs and attitudes towards obese patients.

References

Aussems, M. E., Boomsma, A., & Snijders, T. A. (2009). The use of quasi-experiments in the social sciences: a content analysis. Qual Quant, 12, 1-22. Retrieved from DOI 10.1007/s11135-009-9281-4
Boone, Jr., H. N., & Boone, D. A. (2012). Analyzing Likert Data. Journal of Extension, 50 (2), 1-5. Retrieved from http://www.joe.org/joe/2012april/pdf/JOE_v50_2tt2.pdf
Creel, E., and Tillman, K. (2011).Stigmatization of Overweight Patients by Nurses. The Qualitative Report, 16, 1330-1351.
Levy, Y., & Ellis, T. J. (2011). A Guide for Novice Researchers on Experimental and Quasi-Experimental Studies in Information Systems Research. Interdisciplinary Journal of Information, Knowledge, and Management, 6, 151-161. Retrieved from http://www.ijikm.org/Volume6/IJIKMv6p151-161Levy553.pdf
Yale Rudder Center (YRC). (2010). Measures to Assess Weight Bias. Retrieved from https://shiftthefocus.files.wordpress.com/2012/02/assessingweightbias1.pdf

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