Informing Evidence-based Practice in Nutritional Genomics: An Educational Needs Assessment of Nutrition Care Providers in Canada
Bibliographic record
Abstract
Purpose: To investigate why Canadian nutrition care providers choose, or not, to integrate nutritional genomics into practice, and to evaluate the nutritional genomics training/education experiences and needs of nutrition providers in Canada, while comparing those of dietitians to non-dietitians. Methods: A cross-sectional online survey was distributed across Canada from June 2021 to April 2022. Results: In total, 457 healthcare providers (HCPs) [n = 371 dietitians (81.2%)] met the inclusion criteria. The majority (n = 372; 82.1%) reported having no experience offering nutritional genomics to clients (n = 4 did not respond). Of the 81 respondents with experience (17.9%), the most common reason to integrate nutrigenetic testing into practice was the perception that clients would be more motivated to change their eating habits (70.4%), while the most common reason for not integrating such tests was the perception that the nutrigenetic testing process is too complicated (n = 313; 84.1%). Dietitians were more likely than non-dietitians to view existing scientific evidence as an important educational topic (p = 0.002). The most selected useful educational resource by all HCPs was clinical practice guidelines (n = 364; 85.4%). Conclusions: Both dietitians and non-dietitians express a desire for greater nutritional genomics training/education; specific educational needs differ by type of HCP. Low implementation of nutrigenetic testing may be partly attributed to other identified barriers.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".