Evaluation of the Olo Prenatal Nutrition Follow-up Care for Vulnerable Pregnant Women
Bibliographic record
Abstract
Olo nutritional follow-up care offers vulnerable pregnant women food vouchers, multivitamin supplements, tools, and nutritional counselling to support healthy pregnancy outcomes. Purpose: To evaluate the contribution of Olo follow-up care to nutritional intakes and eating practices, as well as to assess the programme-related experience of participants. Methods: Participants (n = 30) responded to questionnaires and web-based 24-hour dietary recalls and participated in a semi-structured interview (n = 10). Results: Olo follow-up care reduced the proportion of participants below the recommended intake for groups for many micronutrients, with the greatest reduction for folate (by 96.7%), vitamin D (by 93.3%), iron (by 70.0%), calcium (by 50.0%), and zinc (by 30.0%), mainly due to the prenatal multivitamin supplements. Most participants (96.7%) did not follow Olo’s typical recommendations but, if they had, hypothetically they would have consumed an average of 746 additional calories per day and be above the recommendations for excessive intakes of folic acid and iron (100% and 33.3%, respectively). More than half of the participants were moderately to severely food insecure. Olo contributed to reducing the impact of isolation and increased food accessibility and budget flexibility among participants. Conclusion: Olo follow-up care helped reduce the proportion of women below the recommended intake for micronutrients, but revising the food offered and strategies to address food insecurity may be necessary.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".