Optimizing protein intake is a novel strategy to prevent age associated loss of muscle mass and strength in older adults. Such a strategy is still missing for older adults from ethnic minority populations. Protein intake in these populations is expected to be different in comparison to the majority of the population due to several socio-cultural factors. Therefore, the present study examined the dietary protein intake and underlying behavioral and environmental factors affecting protein intake among older adults from ethnic minorities in the Netherlands. We analyzed frequency questionnaire (FFQ) data from the Healthy Life in an Urban Setting (HELIUS) cohort using ANCOVA to describe dietary protein intake in older adults from ethnic minorities in the Netherlands (N = 1415, aged >55 years, African Surinamese, South Asian Surinamese, Moroccan, and Turkish). Additionally, we performed focus groups among older adults from the same ethnic minority populations (N = 69) to discover behavioral and environmental factors affecting protein intake; 40-60% of the subjects did not reach minimal dietary protein recommendations needed to maintain muscle mass (1.0 g/kg bodyweight per day (BW/day)), except for Turkish men (where it was 91%). The major sources of protein originated from animal products and were ethnic specific. Participants in the focus groups showed little knowledge and awareness about protein and its role in aging. The amount of dietary protein and irregular eating patterns seemed to be the major concern in these populations. Optimizing protein intake in these groups requires a culturally sensitive approach, which accounts for specific protein product types and sociocultural factors.
Background: Shared decision-making (SDM) is often considered the ideal for decision-making in oncology. Views of specific groups such as ethnic minorities have seldom been considered in its development. Aim: In this study we seek to assess in oncology if there is a need for adaptation of the current SDM model to ethnic minorities and to formulate possible adjustments. Design: This study is embedded in empirical bioethics, an interdisciplinary approach integrating empirical data with ethical reasoning to formulate normative conclusions regarding a practice. For the empirical social scientific part, a cross-sectional qualitative study will be conducted; for the ethical reflection the Reflective Equilibrium will be used to develop a coherent view on the application of SDM among ethnic minorities in oncology. Method: Semi-structured interviews combined with visual methods (timelines and relational maps) will be held with healthcare professionals (HCPs), ethnic minority patients, and their relatives to identify values steering the behavior of these actors in SDM. In addition, focus groups (FGs) will be held with ethnic minority community members to identify value structures at the group level. Respondents will be recruited through organizations with access to ethnic minorities and collaborating hospitals. Data will be analyzed using a reflexive thematic analysis through the lens of Schwartz’s value theory. The results of the empirical phase will be included in the RE to formulate possible adjustments of the SDM model, if needed. Discussion: The integration of empirical data with ethical reflection is an innovative method in decision-making. This method enables a systematic and profound assessment of the need for adaptation of SDM and the formulation of theoretically and empirically based suggestions for adaptations of the model. Findings of this study may enrich the SDM model.
The report from Inholland University is dedicated to the impacts of data-driven practices on non-journalistic media production and creative industries. It explores trends, showcases advancements, and highlights opportunities and threats in this dynamic landscape. Examining various stakeholders' perspectives provides actionable insights for navigating challenges and leveraging opportunities. Through curated showcases and analyses, the report underscores the transformative potential of data-driven work while addressing concerns such as copyright issues and AI's role in replacing human artists. The findings culminate in a comprehensive overview that guides informed decision-making in the creative industry.
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