For some time, researchers have struggled to find a model that explains the prevalence of a disease within individuals or a community of people. Some of the models that have been in existence, such as the biomedical model and the biopsychosocial model have been used by medical practitioners to examine the prevalence of diseases in human populations. Havranek et al., (2015) argues that medical practitioners have focused only on two aspects of determinants such as physiological and genetic factors while ignoring the third aspect, which is the social factors. By examining the two models, I determine which is the most effective model in examining the prevalence of diseases within a population.
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From my understanding of the biomedical models, all types of diseases have a physical cause. Previously, I believed that physical challenges on the human brain caused mental diseases such as depression and anxiety. In examining the prevalence of diseases within a population, I have discovered that the biomedical approach uses the four ethical principles which call for respect of autonomy, Non-Maleficence, Beneficence, and Justice (Gillon, 2015).
The four principles encourage the biomedical model to implement ethical practices when being applied to a population. This reveals that most medical practitioners, when researching medical procedures such as kidney surgery, X-rays, and urine tests have to consider the ethical practices when performing all these procedures. Therefore, the model is considered an ethical way of examining the prevalence of a disease within a sample population.
Later I realized, however, that despite the biomedical model being considered an ethical method in determining the prevalence of diseases within a population, the model still fails to factor in the social aspect. The model is still unreliable as it does not provide the necessary framework to examine the prevalence of diseases in a community. The model, according to Deacon (2013), is imperialistic as it does not factor in the social factors of other countries other than those in American states. The model also does not follow a specific chain of command.
This is, however, different from the biopsychosocial model, which primarily focuses on research. The biopsychosocial model focusses more on feelings, thoughts, and behaviors that can impact an individual’s health. His is more in line with the social factors that determine the prevalence of a disease within a certain population. This could be probably related to the lifestyle within a specific locale. For example, an area that has limited restriction on smoking can likely to have more people having lung cancer or other respiratory-related diseases. The biopsychosocial model uses experiments, testing, hypothesis, observations, and measurements. Some of the research can focus on answering questions such as “What pushes a person to start smoking?” or “Does good diet result in good health?”
Previously, I thought that the biopsychosocial model focusses primarily on the diagnostic aspect of medical practice. I have, however, discovered that there is more to it as the model also considers the social aspects of a particular population. This is different compared to the biomedical model, which factors biological phenomenon and uses biological diagnostic tools to determine the prevalence of a disease. This concludes that the biopsychosocial model uses both the biological diagnostics and tries to identify the social factors that resulted in the diagnostic. The results from the diagnosis can inform a medical practitioner on the next course of treatment. For example, if a person smokes too much, a doctor can inform the patient to adjust his lifestyle by quitting smoking and determining a new form of treatment.
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This reveals that the biopsychosocial model is a viable means of examining the prevalence of a disease within the human population. For me, the most important aspect is that the model allows medical practitioners to carry out research and make decisions based on the data from the research. This allows the practitioners to factor in the social aspects of the biopsychosocial model, which is absent in the biomedical models. Later I realized that the biopsychosocial model is flawed in that people may be biased in providing some of their personal data, or they might hide some of the critical information from the medical practitioners. This is different from the biomedical model, where ethical considerations limit the model from being a practical solution to addressing the social aspect in medical assessment. However, this is similar to the biopsychosocial model, whereby some people may hide some information about their family history. At first, I knew that the biomedical model analyzed the genetic aspects of an individual. I have, however, come to realize that the biomedical model also takes into account the family history, which might be linked to social factors.
Having analyzed the biopsychosocial model and the biomedical model, I have now understood that both methods play a critical role in examining the prevalence of a disease within a human population. However, it is the biopsychosocial model which goes the extra mile to factor in both the biomedical approach with the biopsychosocial approach to in cooperate the social factor as per the Havranek et al., (2015) recommendations. Therefore, I have significantly improved my outlook as to which model works in examining the prevalence of diseases in the human population.
- Deacon, B. J. (2013). The biomedical model of mental disorder: A critical analysis of its validity, utility, and effects on psychotherapy research. Clinical psychology review, 33(7), 846-861.
- Gillon, R. (2015). Defending the four principles approach as a good basis for good medical practice and therefore for good medical ethics. Journal of medical ethics, 41(1), 111-116.
- Havranek, E. P., Mujahid, M. S., Barr, D. A., Blair, I. V., Cohen, M. S., Cruz-Flores, S., ... & Rosal, M. (2015). Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. Circulation, 132(9), 873-898.
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