Registered Dietitians’ Experiences and Perceptions in Providing Prenatal Nutrition Care in Canada: A Cross-sectional Study
Bibliographic record
Abstract
Purpose: To explore Canadian Registered Dietitians’ (RDs) roles and experiences in prenatal care. Methods: This cross-sectional study utilized an online, anonymous, original survey. Eligible RDs, who are members of Dietitians of Canada (DC) and provide care for pregnancy, were invited to participate through their publicly available online profiles on the DC website. Results: Of the 71 RDs who completed the survey, 97.1% provided nutrition care when requested by the client, 68.8% in times of complications, and 60.0% through referrals. RDs most frequently discussed topics on foods to avoid, supplementation, and healthy eating. Only 4.3% of RDs felt that other prenatal healthcare providers (HCPs) are providing adequate nutritional care, while all (100.0%) RDs believed that they should be the ones providing nutrition care for pregnancy, and most (88.6%) thought they should start providing nutrition counselling during preconception. Most (92.9%) respondents acknowledged that barriers exist in accessing RDs for nutrition advice. Recommendations for improving RD accessibility included increased government funding, involvement in standard care and referrals, awareness, and remote access. Conclusions: Canadian RDs would like to play a larger role in prenatal care through a more integrated approach with other prenatal HCPs and improved access to dietetic services for all pregnant people.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".