Prenatal Nutrition in Team-Based Care: Current Practices and Opportunities for Optimization of Care
Bibliographic record
Abstract
Purpose: To describe prenatal nutrition care currently delivered by Family Health Teams (FHTs) and Community Health Centres (CHCs) in Ontario, from the perspectives of health care providers, and to identify opportunities for improving care. Methods: Ten 1-hour, interdisciplinary focus groups were conducted in FHTs and CHCs, involving a total of 73 health care providers. Focus groups ranged in size from 3 to 11 team members, and at least 3 different professions participated in each group. The shared perspectives and experiences on prenatal nutrition care were collected using a semi-structured interview guide and analyzed using thematic analysis. Results: Limited time was spent on prenatal nutrition education and counselling. Two themes emerged describing gaps in care: (i) providing care in “borderline” high-risk pregnancies (i.e., impaired glucose tolerance) and (ii) providing care around gestational weight gain. Providers envisioned improving services offered by increasing preventative care, empowering providers to provide more nutrition care, facilitating patient self-care, and building a 1-stop shop “medical home”. Conclusions: This study’s findings can guide strategies to mobilize current nutritional knowledge into routine prenatal care, and the shared vision for improvement will inform the routes for new practice that are supported by health care professionals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".