Awareness of possible partialities is important for both public health assessors and policymakers, in a community health assessment: for assessors when crafting and steering studies, and for policymakers when reading study reports and making verdicts.
It is highly important to access risk of bias in all studies review irrespective of preventive unpredictability, in either the results or the authenticity of the studies (Cochrane Methods Bias, n.d.). One example of bias in community health assessment is the study of correlation between health conditions (i.e. stress level) and unemployment. Public health researchers are normally concerned about the health condition of a community or members of community, with its floating peak of unemployment. In this study, we might have a large and diverse sample of unemployed workers from the community population. For instance, how stress level can be related to unemployment amongst members of the community and surveyed the unemployed workers. However, over time those members, who are unemployed for a long period might move, perhaps find works elsewhere. Consequently, they might not be included in a follow-up estimate, a year or two later. If these members are no longer in the study, they may impact the resulted data. This is what we called attrition bias.
According to the Education Commission of the States (n.d.), some of the strategies to employ in justifying attrition researched bias or when research could be trusted are the four questions of:
- What is the research question?
- Does the research design match the research question?
- How was the study conducted?
- Are there rival explanations for the results?
I believed these questions allow community health assessor to come about trusted results, while producing unbiased evidence. Creating balanced substantiation with promoting proof-based decision making are particularly important for the prevention of misleading verdicts. I think the avoidance of self-interested ideology in an assessment process is an additional factor in providing transparent results.
Cochrane Methods Bias. (n.d.). Assessing Risk of Bias in Included Studies. Retrieved from http://bmg.cochrane.org/assessing-risk-bias-included-studies
The Education Commission of the States. (n.d.). How do I know if the research is Trustworthy? Retrieved from http://www.ecs.org/html/educationissues/research/primer/researchtrustworthy.asp
