Scientific Research in Biomedical Sciences - Essay Example

?Running Head: Scientific Research in Biomedical Sciences Scientific Research in Biomedical Sciences [Institute’s Table of Contents Table of Contents 2 Ethical Matrix for Human Volunteers in Pharmacy/Biomedical Research 3 Academic Qualifications in Scientific Research are predominantly Degrees in Philosophy 6 Scientific Truthfulness 8 Significance of Statistical Analyses 10 References 12 Ethical Matrix for Human Volunteers in Pharmacy/Biomedical Research Since few decades, there have been significant developments in the field of science and technology that have resulted in enormous alterations in different processes and procedures globally. In addition, new advancements have resulted in new inquiries and questions regarding legitimacy, effectiveness, and most importantly, ethical standing of the developments that has inclined researchers and experts to carry out research, in order to analyze such aspects. Specifically, the field of biomedical research is one of the sectors that have caused highest number of controversies and ethical issues due to its direct relation with human race and its environment. In the result, theorists and experts (Mepham, pp. 23-39, 2006) have come up with different approaches and methods to resolve issues in decision-making process related to ethical conflicts. In this regard, Ethical matrix (Mepham, pp. 23-39, 2006) is one of the analytical tools of applied ethics that enable individuals, organizations, etc to analyze and scrutinize any issue, in order to come up with a consensus based on ethical understandings. Analysis of ethical matrix has indicated that it has been very beneficial in the establishment of a basic structure needed especially in the field of biosciences and biomedical research. In particular, Ethical matrix resolves the issue by focusing on two components/categories: a set of principles and a list of agents/stakeholders. With the first category of principles, Ethical matrix reemphasizes the significance of principles of morality that allows the decision-maker to come to a decision in an ethical manner. On the other hand, the other category gives value and importance to stakeholders and their opinions (Deane, pp. 51-57, 2009) regarding different biomedical or scientific procedures and processes that play a critical role in coming up with the most efficient ethical decision with the consensus of all the stakeholders. In order to understand the ethical matrix usable for human volunteers in biomedical research, it is essential to comprehend the first category in detail. In particular, ethical matrix has identified three principles that revolve around the notion of respect for “wellbeing, autonomy, and fairness” (Deane, pp. 62-68, 2009), which then integrate with second category of the matrix: interests of the stakeholders. Analysis has pointed out that few ethical theories have played crucial role in creation of the ethical matrix: “utilitarianism (wellbeing), deontology (autonomy), and utilitarianism-deontology (fairness)” (Deane, pp. 51-57, 2009). From this aspect of ethical matrix, one can observe pluralism of its theoretical framework that indicates the wide-ranging applicability of the ethical matrix that is one of the major reasons of significance of ethical matrix in biomedical sciences and scientific research. Now, the discussion will include an example of utilization of ethical matrix in one of the biomedical issues that will be efficient in its understanding in a broad manner. The issue is regarding an innovative research in the field of biomedical research that has indicated the possibility of implantation of a chip in all the citizens that will enable the healthcare organizations to record health history of individuals in their individual electronic chips in their bodies. This will eliminate need of all the paperwork, as healthcare provider will only be scanning chips to know about individual’s health and will then be uploading updated information after the treatment for future purposes. This innovative technology seems very effective and it will be very efficient in lessening costs related to record maintenance of healthcare organizations. However, this requires an ethical debate due to contrary responses from different stakeholders, as there will be need of human volunteers for this innovative biomedical project and may result in adverse outcomes. In this regard, the discussion will now benefit from utilization of an ethical matrix that will enable a broad-ranging understanding of this ethical issue: Respect for Wellbeing Autonomy Fairness Healthcare Organizations Efficient healthcare delivery Freedom to adopt the technology Fair implementation of trade and law Citizens Healthcare safety and high quality of life Independent decision-making Adequate and immediate healthcare response Human Volunteers Possibility of adverse health impact Should have the power to decide regarding adequacy of the chip Should receive intrinsic value for their contribution, rather than being treated it as experimental subjects Society Economic benefits Protection of humanistic values Sustainability of the human body Abovementioned illustration of ethical matrix (Coors, pp. 20-25, 2003) indicates different aspects of an ethical issue that identifies the significance of this analytical tool, which allows the concerned authorities to come up to a consensus after going through perspectives of all the interested stakeholders. In brief, the abovementioned example of ethical matrix is an efficient example of the framework for the use of human volunteers in biomedical research along with summarization of different stakeholders’ perspectives and their values. Academic Qualifications in Scientific Research are predominantly Degrees in Philosophy In the majority of universities, completion of PhD usually involves two years of full-time study, as well as the submission of a thesis consisting of a body of original scientific research carried out by the applicant. It is usual for learners enrolled into a PhD program to be initially enrolled for the degree of MPhil, and after that transfer to the PhD upon successful completion of the first (or second) year of study (Scales et al, p. 102, 2004). This will frequently require the submission of a short research report or thesis by the student, and maybe an oral assessment or presentation (Scales et al, p. 102, 2004). Besides, nearly all educational institutions let examiners to suggest grant of MPhil if the applicant's research paper is considered not to be of the required length or criteria for a doctorate. Nonetheless, a number of students enroll in MPhil with no plan of advancement to PhD, either because of personal situation or because of their selected research topic having unsatisfactory scope for a PhD. In some institutes of higher education, MPhil research degree can be attained after just one year of study (Scales et al, p. 102, 2004). Undertaking PhD or MPhil will enable the individual to take unique as well as major part in the development of understanding in the selected subject area. Because of the focus and significance of scientific research in PhD, it is usually an assumption that generating new facts is the most important objective of PhD. Although generating new facts is part of the PhD education, the key purpose of doing PhD is to be a capable associate who can carry out self-regulating scientific research in the selected area (Dee, p. 84, 2006). The aptitude to conduct scientific research within a particular area needs extensive understanding of that area, familiarity with interrelated areas, as well as the knowledge of working on research problems, that is, problems whose results are not identified. To develop these significant skills, the majority of PhD programs have three factors in them - a little course work to offer the span of understanding, some methods to extend the intensity of understanding in the selected area of study, and a research paper that offers the experience of working on problems arising in scientific research. Lacking this, there is no successful method to express that the capability to carry out research as well as correspond the results has been developed. Nonetheless, within the general research career, it is having these abilities that is more significant, mainly if one would like to work in business where the requirements may alter and concerns that a researcher deals with may be somewhat changed from the ones selected in PhD. In academics, though, the real outcomes are just as essential as the evaluative procedures often concentrate on the value, as well as amount of scientific research work that has been completed in the PhD (Clare & Hamilton, p. 128, 2003). This makes PhD a self-determined and self-educated degree; however, the supervisor softly helps the process. The program as well as teacher assists mainly in generating an environment and setting within which the researcher gets encouraged to do extremely well. What is essential is to become skilled at accurately formulating a problem as well as applying appropriate procedures to create results that promote the state of awareness regarding the problem. Scientific Truthfulness The level of trust that has exemplified science and its link with society, has added to a phase of unmatched scientific output. However, this trust will carry on only if the scientific society dedicates itself to representing as well as spreading the standards linked with ethical scientific behaviour. Consideration of trustworthy conduct in addition to the reliability of the research encourages the assessment of that project from the viewpoint of ‘every party to it’, instead of from the viewpoint of the decision-makers only (Shapin, p. 176, 1995). Critics on trust, repeatedly imply that trust is obligatory because the ‘trusting party’ cannot manage or supervise the unreliable party's performance. Indeed, it is true that the failure to manage or supervise behaviour is an aspect in the ‘need for trust’. Laboratory supervisors often openly confess that the level of data gathered in their laboratories makes it unfeasible for them to individually check even their personal graduate students’ research results; and would try to do so merely if some reason was given to make a result uncertain. As a result, the situation that, at least as said by a few, place the stage for delinquency, is currently even more common. Restrictions on the efficiency of supervising are particularly clear where research associates are from different disciplines. Two associates from different disciplines connect in a group effort would not gain from complete insight of the other's behaviour, or even the capability to direct the other's actions. Even though one associate might be capable to identify a little acts of gross ineffectiveness or malfeasance on the part of the other, neither associate would completely acknowledge the inferences of everything that he saw the other do and might have just a slight idea of the way to advance the other's performance. During many situations of group effort, responsible behaviour has no satisfactory replacement (Merwe et l, p. 93, 2009). During the recent past, as conversation about scientific research truthfulness was polarized and controlled by official as well as quasi-official considerations, the role of intent during research delinquency was overstated, and a basic distinction between honest errors on the one hand and deliberate acts of unlawful activity on the other, recognized unlawful activity with intent to betray. A number of inaccuracies are honest mistakes - even a vigilant as well as reliable individual might make them. Others are careless or casual - they reveal inadequate concern to meet some benchmarks. Nonetheless, not every benchmark is ethical benchmark. As a result, not all, but a few sloppy errors are ethically at fault (Shapin, p. 201, 1995). It is irresponsible to be sloppy regarding issues for which one has ethical liability. If a medical doctor sews up the patient with some surgical tool inside, the surgeon is responsible for carelessness. This act is ethically chargeable although the surgeon does not mean to hurt. If somebody drips his coffee on his clothes, that is careless as well as casual. However, in view of the fact that the standards dishonored are not ethical standards, the negligence is not ethically chargeable; it is not neglectful or irresponsible behaviour (Wallace, p. 53, 1996). Thus, differentiating between recklessness or carelessness and deception paves the path to shed light on the conditions that contribute to unlawful activity within scientific research. Significance of Statistical Analyses The amount of substantiation necessary to believe that an event is doubtful to happen unintentionally is recognized as the significance level. It is the probability of studying data at least as excessive as that studied, only if the ‘null hypothesis’ is right. If the acquired value is small then it can be supposed that either the ‘null hypothesis is false’ or an odd event has taken place. It is worth emphasizing that significance level does not have any recurring sampling analysis (Sprinthall, p. 104, 2006). More characteristically, the significance level of a test is the probability of incorrectly refusing the ‘null hypothesis’ is no more than the known probability. This lets the test to be carried out by means of non-momentous statistics, which has the benefit of lessening the computational trouble at the same time as wasting a little information. The significance level is generally indicated by ?. Popular levels of significance are “10 % (0.1), 5 % (0.05), 1 % (0.01) and 0.1 % (0.001)” (Ott & Longnecker, p. 98, 2008). If an analysis of significance gives the value lesser than the significance level, the null hypothesis is therefore discarded. This sort of results is informally identifiable as statistically significant. Selecting level of significance is a subjective job; however, for a number of functions, a level of ‘5 percent’ is selected, for no better explanation than that it is predictable. In a few conditions, it is suitable to express the arithmetical significance as ‘1 – ?’ (Ott & Longnecker, p. 67, 2008), usually, when inferring a stated significance, one should be cautious to note what exactly is being checked statistically. Various significance levels exchange countervailing results. Lesser levels of significance boost assurance in the verification of significance, although run a greater risk of failing to discard a false null hypothesis, and so have a reduced amount of statistical control. The choice of significance level, as a result, unavoidably involves a settlement between significance and control, and subsequently between the “Type I error and the Type II error” (Ott & Longnecker, p. 67, 2008). Influential tests (generally tests with additional subjects or duplications) can preclude this option to a subjective amount. Statistical significance believed to be the assurance one has in a provided outcome. Within a relationship study, it is ‘dependent’ on the ‘relative’ variation between the groups contrasted, the sum of measurement in addition to the noise linked with the measurement. In other words, the assurance one has in a provided outcome being non-random - to be precise, it is not a result of probability - relies on the signal-to-noise ratio as well as the sample size (Myers et al, p. 194, 2010). Expressed statistically, the assurance that the outcome is not by random probability is offered by the following formula: Assurance = (Signal / Noise) X vSample Size The assurance of an outcome - as well as its related assurance interval - is not reliant on effect size only. If the sample size is huge and the noise is small, a small effect size can come under calculation with great assurance. Whether a small effect size is considered significant or not is reliant on the situation of the events in comparison. References Clare, Judith and Hamilton, Helen. 2003. Writing Research: Transforming Data into Text. Churchill Livingstone. Dee, Phil. 2006. Building a Successful Career in Scientific Research. Cambridge University Press. Merwe, Hugo-van-der. Baxter, Victoria, and Chapman, Audrey R. 2009. Assessing the Impact of Transitional Justice: Challenges for Empirical Research. United States Institute of Peace Press. Myers, Jerome L. Well, Arnold D. and Lorch, Robert F. 2010. Research Design and Statistical Analysis. Routledge Academic. Ott, Lyman R., and Longnecker, Michael T. 2008. An Introduction to Statistical Methods and Data Analysis. Duxbury Press. Scales, Peter C. Leffert, Nancy, and Lerner, Richard. 2004. Developmental Assets: A Synthesis of the Scientific Research on Adolescent Development. Search Institute Press. Shapin, Steven. 1995. A Social History of Truth. University Of Chicago Press. Sprinthall, Richard C. 2006. Basic Statistical Analysis. Allyn & Bacon. Wallace, James. 1996. Ethical Norms, Particular Cases. Cornell University Press. Mepham, T. B. 2006. Ethical Matrix. LEI. Deane, Peter. 2009. Research Integration using Dialogue Methods. ANU E Press. Coors, Marilyn E. 2003. The Matrix. Rowman & Littlefield. Read More