Foresight in health sciences using Causal Layered Analysis method

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Abstract

Background: Development in health is not possible without progress of science. Rapid changes in the various areas make the future health system more complex and risky. Therefore, foresight of health sciences is very important. Methods: This futures studies was conducted in 4 steps; also, literature and documents review, statistics and information review, focus group discussions, working group, and scenario planning were used. Causal layered analysis (CLA) was used for data analysis and syntactic as main frameworks. Results: The findings in legal health sciences documents revealed that the value system was not defined clearly and coherently and that logical linkage among myths, discourse, and social structural layers was ambiguous. In trend analysis, 24 trends were recognized; however, political and economic streams were strong, independent, and uncertain factors which created 4 main scenarios although the social and environmental factors divided them into 16 subscenario tunnels. Postmodern discourse in probability scenarios will be dominant and science will be understood as tools for generation of wealth. University structure will be decentralized and transformed into similar R&D to join the health industry, and our quantitative growth (articles, disciplines, and students) in health sciences will decrease. Conclusion: If the current trends (probability scenarios) continue, we will move to an undesirable situation. The main challenge in this regard is the lack of a unique and dominant discourse in health sciences based on the Islamic Republic of Iran doctrine. Therefore, in this study, shifting the paradigm by a new approach and discipline in the health sciences is suggested.

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Haghdoost, A. A., Pourhosseini, S. S., Emami, M., Dehnavieh, R., Barfeh, T., & Mehrolhassani, M. H. (2017). Foresight in health sciences using Causal Layered Analysis method. Medical Journal of the Islamic Republic of Iran, 31(1), 1–8. https://doi.org/10.18869/MJIRI.31.84

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