Impact of implementation of a breastfeeding education program: Prospective evaluation of a community medical student-led program
Bibliographic record
Abstract
Introduction/Objectives: Among children born in 2019, only 77% reported having ever been breastfed in Oklahoma, while the national average of that year was 83%. These reported rates expose the gap in breastfeeding in Oklahoma. Specifically, in Cherokee County within Oklahoma, that percentage is 78%. This deficit in the percentage of breastfed infants in Oklahoma is significant because breastfeeding provides an array of health benefits to both baby and mother. In babies, breast milk provides essential nutrients and natural passive immunity. Breastfeeding has also been shown to reduce the risks of asthma, obesity, and type I diabetes. It benefits the mother breastfeeding by reducing the chances of high blood pressure, type 2 diabetes, ovarian and breast cancer. While breastfeeding is the gold standard for infant nutrition, there are several barriers to breastfeeding that the mother can face that should be considered. Issues like latching, concerns for the infant's weight and growth, the limited choices of medications that the mother can use, lack of support from family or in the workplace, lack of education about breastfeeding, and cultural stigmas all contribute to if or how prolonged breastfeeding occurs.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".