Developing a Prenatal Nutrition Tool: A Process of Evidence, Collaboration, and Consultation
Bibliographic record
Abstract
The Prenatal Nutrition Tool was created for care providers that work with pregnant clients and aims to support focused conversations on nutrition topics that influence maternal and infant health outcomes. A systematic 9-step product development process that combined findings from the literature with perspectives of nutrition experts and care providers was used to develop the tool. The results of a literature review and a modified Delphi Process (to obtain expert opinion) laid the foundation for the tool content. The final tool incorporated client feedback. More specifically, client feedback helped to refine tool questions. The tool consists of 2 parts: a questionnaire (written survey) and a conversation guide. The questionnaire covers 4 key themes (pregnancy weight gain, multivitamins, life circumstances, overall food intake) in 13 questions. The conversation guide utilizes public health nutrition guidance documents to lead care providers in focused discussions with clients. The tool is not intended to be a screening tool for medical conditions or replace an in-depth prenatal nutrition assessment. The tool can be accessed by any care provider in Canada on the Alberta Health Services website at Prenatal Nutrition Tool | Alberta Health Services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.382 | 0.328 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.012 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".