An Electronic Health Record-based Intervention to Facilitate Primary Care Referrals to WIC: A Retrospective Cohort Study (Preprint)
Bibliographic record
Abstract
BACKGROUND: Despite benefits of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), many eligible children remain unenrolled. OBJECTIVE: This study evaluated responses to an electronic health record (EHR)-embedded federal nutrition program (FNP) participation screening and WIC referral tool implemented within eight clinics during <5-year-well visits. METHODS: Structured EHR data from July 2020-October 2023 were extracted to summarize screening, referral acceptance, and WIC enrollment outcomes and patient demographics. Multivariable logistic regression examined patient-level predictors of WIC non-enrollment at first screening, referral acceptance, and enrollment among those accepting a referral, with stratified analyses by clinic type. RESULTS: Among 5,385 children, mean age at initial screening was 10.7 months (SD 14.2), 52% newborn-aged, 37% Hispanic, and 88% Medicaid-insured. Among screened patients (n=3,606), 34% were not enrolled in WIC at first screening (n=1,235). Medicaid coverage, academic clinic setting, and non-Hispanic Black or Hispanic race and ethnicity were associated with lower odds of WIC non-enrollment. Among those not enrolled with a documented response to referral (n=819), 59% accepted (n=488); acceptance was higher among non-Hispanic Black and Hispanic patients, newborn-aged patients, those with Medicaid coverage, and those seen in academic clinics. Among referral acceptors with follow-up (n=438), 61% were enrolled at last screening (n=265), with higher odds among newborn-aged patients and those in academic clinics. CONCLUSIONS: An EHR-based automated intervention can facilitate screening and referral to WIC. WIC participation at first screening, referral acceptance, and enrollment after referral varied by sociodemographic characteristics, suggesting opportunities to improve equitable access through health system-based approaches. CLINICALTRIAL: Not applicable.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".