Prenatal Nutrition Care in Alberta: The Perspectives of Pregnant Women and Registered Dietitians
Bibliographic record
Abstract
Introduction: Optimizing women’s diets in pregnancy improves maternal and child health outcomes; however, the best format for supporting women’s nutrition goals in pregnancy is not clear, and access to dietetic services is not standard in prenatal care in Alberta. This study explored women’s perceptions about access to Registered Dietitians (RDs) throughout pregnancy and RDs experiences providing prenatal nutrition counselling. Methods: Two studies were conducted. Study A: Pregnant women completed a short survey while attending a prenatal appointment in a large prenatal clinic. The survey assessed women’s perspectives about accessing dietetic services during pregnancy. Survey data were analyzed using descriptive statistics. Study B: RDs participated in either a semi-structured phone interview or a focus group and described their experiences working with pregnant women. Data were analyzed using thematic analysis. Results: One hundred pregnant women completed the survey. Ninety percent indicated that they had not seen a RD at this time in pregnancy, and 48% reported that they would like to access a RD in pregnancy, if available. Dietitians discussed the diversity of women’s concerns and the challenges to providing prenatal nutrition support. Conclusions: Women have nutrition-related questions during pregnancy. Dietitians experience challenges providing services in the current care systems.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".