Trend Article Analysis

Trend Article Analysis Name Course Date Instructor Trends in research date back hundreds of years to when certain findings, treatments, studies, and research projects started new trends and ways in the field of psychology. The following paper summarizes an article on optimism bias in psychology and clinical research, and examines how the trend and change have affected research methodology. Statistics from randomized trails and quantitative methods analyze optimism bias in clinical research and psychology.

After the summary of the article, the impact or implications of the research methodology elated trend, how optimism bias has affected the past and future of the field of psychology is broken down. Article Summary In an article by Chalmers and Matthews (2006), the authors write about the topic of optimism bias and the impacts optimism bias has on the field of psychology and research methods regarding treatments.

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Optimism bias, also known as citation bias, is how studies of new treatments are more likely to cite previous studies reporting positive results than equally valid studies with disappointing results (Chalmers & Matthews, 2006, p. ). Optimism bias takes old studies and integrates the study and itations into a new study claiming to work Just as well if not better than the old Just on the basis alone of the old study working. Upgraded or different versions of studies and methods need more than Just proof the old methods or research worked. Unrealistic expectations are Just one implication of optimism bias according to Chalmers and Matthews (2006). Patients and clinicians suffer from the unrealistic expectations offered by optimism bias.

An example used in the article of optimism bias, a study conducted on a new radiotherapy treatment for head and neck cancer uffers gave high levels of optimism to patients claiming the treatment would cut the mortality rate in the individuals suffering by 30%. The trial found no evidence of the new treatment as any type of advance, and the false expectations already had a trickledown effect on patients hearing the optimistic news. In countering optimism bias, one of the few ways according to Chalmers and Matthews (2006, p. 1) is “to present systemic evidence to patients and clinicians involved in randomized trials. Ethical and scientific practices do no yet prevent optimism or citation bias in studies done. Other than deterring individuals from an onest and true result from a study, optimism bias is a discouraging replication of already promising early studies.

Optimism bias has been a problem with studies done on new and innovative versions of old and trusted treatments and research. Unfortunately, the regulations of optimism bias are slim to none and studies use old data on reliable research to promise the same from new and innovative research or treatments.

Related to the Future Optimism bias or citation bias is a dangerous part of research. Reading the article, one can see how mentions and uses of references of previous research can sway the xpectations of an individual reading on something new. Optimism bias greatly affects the future of research methods and trends.

A clinician researching a newer treatment for obsessive compulsive disorders for a young adult can come across optimism bias in the articles and research found.

The clinician does the usual research and leg work to ascertain if the treatment even works and finds a new spin on an old treatment. The clinician reads enough to feel confident the new treatment will work for the patient and passes the information and instructions on to the patient. The bias in the research articles regarding the old treatment masks the eality of the new treatment. The new treatment fails and later down the road research comes out supporting the failure of the new form of the treatment.

This trend in research methodology can cause unrealistic expectations to happen with the clinician and the patient. Regulated or better regarded research in the future can be better trusted. In randomized trials, doubt must remain if clinicians “are genuinely observing the ethical requirement of uncertainty’ (Chalmers ; Matthews, 2006, pg. 2) After being made aware of optimism bias, one can be fearful of any new research or implications on an old and effective treatment or research tudy. Individuals rely on the reliability and validity of research of any sort.

The more experience a clinician has on a selected topic, the less optimism bias will affect the clinician.

Research methods should have slight alterations to take into account the optimism bias factor. Detailed research is recommended on a selected topic so the clinician or any person researching anything can have a true idea of the reality of the content in the research. Optimism bias is closely related to leading. The research methodology of something is a process conducted to gain A from B, or example, an answer from a question or a paper from research.

A researcher should have the confidence that research conducted holds true and does not rely on past outcomes to prove future outcomes that have not even happened yet.

In research, many can feel confident because an old study and method worked in the past, a change to the same study works Just as efficiently. Because of optimism bias, the idea misleads individuals and gives false and unrealistic expectations. After researching an article on optimism bias, the topic has been a problem in research and will continue to be a problem in research.

Optimism bias takes old studies and intergrades the study and citations into a new study claiming to work just as well if not better than the old Just on the basis alone of the old study working. Chalmers ; Matthews (2006).

The future of research is affected by optimism bias by randomized research not being reliable because of optimism bias. Until its persistence is addressed, optimism bias casts doubt over randomized research and the way clinicians genuinely observe the ethical requirement of uncertainty in a research study. References Chalmers, 1. , ; Matthews, R. (2006, February 11).

What are the implications of