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Integrating Social Determinants in Decision-Making Processes for Health: Insights from Conceptual Frameworks-the 3-D Commission.

ABSTRACT: The inclusion of social determinants of health offers a more comprehensive lens to fully appreciate and effectively address health. However, decision-makers across sectors still struggle to appropriately recognise and act upon these determinants, as illustrated by the ongoing COVID-19 pandemic. Consequently, improving the health of populations remains challenging. This paper seeks to draw insights from the literature to better understand decision-making processes affecting health and the potential to integrate data on social determinants. We summarised commonly cited conceptual approaches across all stages of the policy process, from agenda-setting to evaluation. Nine conceptual approaches were identified, including two frameworks, two models and five theories. From across the selected literature, it became clear that the context, the actors and the type of the health issue are critical variables in decision-making for health, a process that by nature is a dynamic and adaptable one. The majority of these conceptual approaches implicitly suggest a possible role for data on social determinants of health in decision-making. We suggest two main avenues to make the link more explicit: the use of data in giving health problems the appropriate visibility and credibility they require and the use of social determinants of health as a broader framing to more effectively attract the attention of a diverse group of decision-makers with the power to allocate resources. Social determinants of health present opportunities for decision-making, which can target modifiable factors influencing health-i.e. interventions to improve or reduce risks to population health. Future work is needed to build on this review and propose an improved, people-centred and evidence-informed decision-making tool that strongly and explicitly integrates data on social determinants of health.

SUBMITTER: Martins DC

PROVIDER: S-EPMC8415431 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Though individual categorization or decision processes have been studied separately in many previous investigations, few studies have investigated how they interact by using a two-stage task of first categorizing and then deciding. To address this issue, we investigated a categorization-decision task in two experiments. In both, participants were shown six faces varying in width, first asked to categorize the faces, and then decide a course of action for each face. Each experiment was designed to include three groups, and for each group, we manipulated the probabilistic contingencies between stimulus, category assignments, and decision consequences. For each group, each participant received three different sequences of category response, category feedback, decision response, and decision feedback. We found that participants were only partially responsive in the appropriate directions to the contingencies assigned to each group. Comparisons of results from different sequences provided evidence for empirical interference effects of categorization on decisions. The empirical interference effect is defined as the difference between the probability of taking a hostile action in decision-alone conditions and the total probability of taking a hostile action in categorization-decision conditions. To test competing accounts for multiple empirical results, including two-stage choice probabilities and empirical interference effects, we compared a quantum cognition model versus a two-stage exemplar categorization model at both aggregate and individual levels. Using a Bayesian information criterion, we found that the quantum model provided an overall better model fit than the exemplar model. Although both models predicted empirical interference effects, the exemplar model was able to generate probabilistic deviation by incorporating category information of the first stage into the feature representation of the subsequent decision stage, while the quantum model produced interference effect by superposition, measurement, and quantum entanglement.

Project description:Food is an important determinant of health, featuring prominently in the Sustainable Development Goals. The term "big data" is seldom used in relation to food, partly because food data are scattered across different sectors. The increasing availability of food-related data presents an opportunity to glean new insights on food and food systems. These insights may enhance the quality of products and services and improve decision-making on optimizing food availability, all to the end of producing better health. Yet, knowledge gaps remain about the unique opportunities and challenges linked to big data on food and their use in decision-making. This scoping review explored the available literature linking food with big data and decision-making, using the following research question: What is the current literature on data about food, and how are these data used in decision-making? We searched PubMed until 29 February 2020 and Embase, Web of Sciences, and the Cochrane Database of Systematic Reviews until 8 March 2020. We included studies written in English and conducted narrative analyses to identify relevant themes from included studies. Sixteen studies fulfilled our eligibility criteria, including big data analyses, modelling studies, and reviews. These studies described the added value of using big data and how evidence from big data had or can be used for decision-making, as well as challenges and opportunities for such use. The majority of the included studies examined the link between food and big data, while hypothesizing of how these insights could inform decision-making, including policies, interventions, programs, and financing. There were only two examples wherein big data on food informed decision-making directly. The review highlights several false dichotomies in how the subject is approached in the literature and the importance of context, both between and within countries, in shaping the availability and types of data that can be used as meaningful evidence to inform decision-making. This review shows the paucity of research around the intersection of food, big data, and decision-making, as well as the potential in using big data on food systems to the end of informing decisions to improve the health of populations. Future research and decision-making around health systems can benefit from examining the full spectrum of perspectives on the subject. Future research and decision-making around health systems can also employ the steadfast embrace of technology, which will potentially reduce disparities in big data availability, to the end of improving the health of populations.

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Integrating Social Determinants in Decision-Making Processes for Health: Insights from Conceptual Frameworks-the 3-D Commission.

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