DNP 801 How could a quality improvement project (DPI Project) be affected if the research used had bias?

DNP 801 How could a quality improvement project (DPI Project) be affected if the research used had bias?

DNP 801 How could a quality improvement project (DPI Project) be affected if the research used had bias?

Bias is any trend or deviation from the truth in data collection, data analysis, interpretation, and publication that can cause false conclusions. Bias can occur either intentionally or unintentionally. Intention to introduce bias into someone’s research is not moral. Nevertheless, considering the possible consequences of biased research, it is almost equally irresponsible to conduct and publish biased research unintentionally (Gardenier JS, Resnik DB, 2019). Bias distorts the truth, it interferes with the ability to truly understand the environments around us. It is the most challenging obstacle for researchers. It is worth pointing out that every study has its confounding variables and limitations. Confounding effects cannot be completely avoided. While Personal bias happens when the research results are altered due to personal beliefs, customs, attitudes, culture, and errors among many other factors. It also means that the researcher must have analyzed the research data based on his/her beliefs rather than the views perceived by the respondents (Scott K, McSherry R, 2019) In research studies having a well-designed research protocol explicitly outlining data collection and analysis can assist in reducing bias. Feasibility studies are often undertaken to refine protocols and procedures. Bias can be reduced by maximizing follow up and where appropriate in randomized control trials analysis should be based on the intention to treat principle, a strategy that assesses clinical effectiveness because not everyone complies with treatment and the treatment people receive may be changed according to how they respond. Bias research has been criticized for lacking transparency in relation to the analytical processes employed (Smith, J., & Noble, H. 2018).

A quality improvement DPI project could be affected or reduced by the random selection of participants since I am using a clinic setting and in the case of clinical trials randomization of participants into comparison groups. Also, some participants might withdraw from the study or be lost due to failed follow-up. This can result in sample bias or change the characteristics of participants in comparison groups.  In qualitative research purposeful sampling has advantages when compared to convenience sampling in that bias is reduced because the sample is constantly refined to meet the study aims. Premature closure of the selection of participants before analysis is complete can threaten the validity of a qualitative study. This can be overcome by continuing to recruit new participants into the study during data analysis until no new information emerges, known as data saturation.

References

 

Gardenier JS, Resnik DB. The misuse of statistics: concepts, tools, and a research agenda. Account Res. 2019;9:65–74. http://dx.doi.org/10.1080/08989620212968. [PubMed] [Google Scholar]

 

Scott K, McSherry R. Evidence-based nursing: clarifying the concepts for nurses in practice. Nursing in Critical Care, 2019: 3; 67-71 p 1089.

 

Smith, J., & Noble, H. (2018). Bias in research. Evidence-Based Nursing, 17(4), 100-101. https://doi.org/10.1136/eb-2018-101946

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Bias is when there is undue favor for or against a particular thing, person or group in an unfair way while discounting the obvious truth of the others or by distorting the truth or discarding the facts as presented either personally or in academic research (Oxford Dictionary, 2019). In any research, bias can happen at any time. This is when there is an error in the systematic way used to conduct the research. Such as in the study design, data collection, sampling, interventions, experiments and controls, as well as in analyzing and the reporting of results (Enago Academy, 2021). Bias is one of the reasons that research is not valid, it reduces the credibility and accuracy of the researcher. Some researchers include their personal beliefs which influences their methods hence they become impartial (Enago Academy, 2021). Most qualitative research is prone to emotional biases especially in the social, political, religious and psychological fields as compared to the scientific fields that deals with numbers and statistics (Enago Academy, 2021). There are different types of Biases starting with the design bias, data collection with selecting of samples and participants, analyzing the data, process bias and publication bias (Enago Academy, 2021). There are also other types of biases in research such as race bias, social class bias and gender bias (Alcalde-Rubio, Hernández-Aguado, Parker, Bueno-Vergara, & Chilet-Rosell, 2020). So, to reduce the possibility of bias in research, the researcher should be aware of themselves totally, widen their range of possibilities and sample participants, and be careful of choice of vocabulary (Enago Academy, 2021). There is also the observation bias known as the Hawthorne effect-when participants know that they are being observed by the researcher, they change their answers or behavior, confirmation bias- the researcher looks only for information or patterns to confirm their ideas while recall bias is when participants recall events which may be recalled in a distorted form (MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. (n.d.).

A quality improvement DPI project could be affected if they do not meet the comprehensive standard for inclusion in the research. Some DPI projects did not state a clear evidence gap and may involve so many different settings participants from different age ranges and then they end up not fully describing the implementation process or the implementation is not appropriate for all the age groups. Some did not fully describe their methods, intensity of activities of the participants or the implementers or the involvement of the site all that can lead to bias of the research. The credibility of the site will be affected and they may lose their accreditations and licenses. Also, patients may not want to go to that site any longer (Wells, Tamir, Gray, Naidoo, Bekhit, & Goldmann, 2018).

 

The article, Association between gender and stoke recurrence in ischemic stroke patients with high-grade carotid

DNP 801 How could a quality improvement project (DPI Project) be affected if the research used had bias
DNP 801 How could a quality improvement project (DPI Project) be affected if the research used had bias

artery stenosis by Chen, Weng, Wu, & Huang, (2021) illustrates some of the biases that can discredit any research. In this article, a total of 372 participants were used of which 273 were males and only 99 were females. I feel that the ratio of males to females is a gender bias for the researcher to conclude that the male gender had a higher rate of increased risk for stroke recurrence compared to the female gender.  It the number for both was comparable then the readers may be willing to accept this research. Also, the article points out that some gender differences that was conducted in other research was pointed out but the article still remained confusing. Another bias is the sample size is small to conclude that the prevalence of stroke recurrence is higher in males-which may be caused by smoking in males-than females. Also, they had some unmeasured confounders that may have influenced their conclusions. This bias has led them to propose the need for aggressive treatments for males and females may be treated casually which may lead to serious injuries for the females. I believe that this bias has affected the validity of the research because the sample size is not representative of the entire groups of males or females. It could still be viable research for my DPI project because I will look at what worked or not and attempt to improve on it (Chen, Weng, Wu, & Huang, 2019).

 

References:

 

Alcalde-Rubio, L., Hernández-Aguado, I., Parker, L. A., Bueno-Vergara, E., & Chilet-Rosell, E. (2020). Gender disparities in clinical practice: Are there any solutions? Scoping review of interventions to overcome or reduce gender bias in clinical practice. International Journal for Equity in Health19(1). https://doi.org/10.1186/s12939-020-01283-4

 

Chen, C., Weng, W., Wu, C., & Huang, W. (2019). Association between gender and stoke recurrence in ischemic stroke patients with high-grade carotid artery stenosis. Journal of Clinical Neuroscience67, 62-67. https://doi.org/10.1016/j.jocn.2019.06.021

Enago Academy. (2021, April 28). Dealing with bias in academic researchhttps://www.enago.com/academy/dealing-with-bias-in-academic-research/

 

Oxford Dictionary. (2019, September 16). Bias. Oxford Languages | The Home of Language Data. https://www.oxforddictionaries.com

MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. (n.d.). Understanding health research · Common sources of biashttps://www.understandinghealthresearch.org/useful-information/common-sources-of-bias-2

 

Wells, S., Tamir, O., Gray, J., Naidoo, D., Bekhit, M., & Goldmann, D. (2018). Are quality improvement collaboratives effective? A systematic review. BMJ Quality & Safety27(3), 226-240. https://doi.org/10.1136/bmjqs-2017-006926