Knowledge and Attitudes of Canadian Consumers and Health Care Professionals Regarding Nutritional Genomics
Bibliographic record
Abstract
With advances in the field of nutrigenomics, commercial laboratories have begun marketing genotyping services, nutritional advice, and dietary supplements "tailored" to match individual genetic predispositions. Although primarily offered by American companies, these services are available to Canadian consumers via the Internet. Qualitative research in the form of focus groups with members of the Canadian public was undertaken to assess the current level of understanding of and receptivity toward this new genomic application. Additionally, focus groups with health care professionals (physicians, pharmacists, dieticians, nutritionists, and naturopaths) investigated their interest in integrating nutrigenomics into health care delivery, and their capacity to do so. Gauging knowledge and attitudes early in the introduction of a new technology serves to identify potential "blind spots" regarding the ethical, legal, and social implications. Preliminary results indicate consumers believe potential benefits of nutrigenomics outweigh risks, while health care professionals express more skepticism. Both groups agree that more public education about nutrigenomics is needed and that regulatory oversight should ensure consumer protection.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".