Nutrigenetic Testing for Personalized Nutrition: An Evaluation of Public Perceptions, Attitudes, and Concerns in a Population of French Canadians
Bibliographic record
Abstract
BACKGROUND/AIMS: This study aimed to evaluate attitudes, perceptions, and concerns about nutrigenetic testing for personalized nutrition in the general population of the province of Quebec in Canada. METHODS: A total of 1,425 individuals from the province of Quebec completed a 37-question online survey on nutrigenetics and were included in analyses. The χ2 test was used to test for associations between categorical variables. RESULTS: The majority of the participants (93.3%) considered dietitians the best professionals to give personalized dietary advice based on nutrigenetic testing. The main reported advantage for nutrigenetic testing was "health" (23.5%), followed by "disease prevention" (22.2%). Among the disadvantages, "no disadvantage" (24.4%), followed by "diet restriction" (12.9%) were mostly reported. The 2 major concerns raised were accessibility to genetic testing by telemarketing companies and spammers (51.8%), and solicitation by companies using personal genetic data to sell products (48.6%). CONCLUSIONS: French Canadians generally have a positive attitude towards nutrigenetics and consider its use to have many benefits. They expressed concern about possible confidentiality issues associated with the management or property of genetic test results. Education about such issues is needed. Overall, our findings suggest that the population is interested in more extensive use of nutrigenetics in health management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".