Socio-ethical Analysis of Equity in Access to Nutrigenomics Interventions for Obesity Prevention: A Focus Group Study
Bibliographic record
Abstract
The goal of nutrigenomics is to develop nutritional interventions targeted to individual genetic make-up. Obesity is a prime candidate for nutrigenomics research. Personalized approaches to prevention of diseases associated with obesity may be available in the near future. Nevertheless, in the context of limited resources, access to a nutrigenomics personalized health service raises questions around equity. Using focus groups, the present qualitative research study provides empirical data on ethical concerns and values surrounding the nutrigenomics-guided personalized nutrition for obesity prevention. Eight focus groups were convened including 27 healthy individuals and 21 individuals who self-identified as obese or at risk of obesity. The transcripts of the focus group were analyzed according to the qualitative method of grounded theory. Responsibility, reciprocity, and solidarity emerged as the key ethical criteria perceived by the respondents to be significant in terms of how health professionals should determine access to personalized nutrition services. Still, exclusion of individuals from specific nutrigenomic services is likely to conflict with the imperatives of medical deontology and contemporary social consensus. The representation of equity in this paper is novel: it considers the intersection of nutrigenomics and personalized nutritional interventions specifically in the context of limited public resources for health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".