South Carolina’s Choose Well Initiative to Reduce Unintended Pregnancy: Rationale, Implementation Design, and Evaluation Methodology
Bibliographic record
Abstract
W e describe the implementation of Choose Well (CW), a statewide contraceptive access initiative ongoing in South Carolina, and the external evaluation of CW conducted at East Tennessee State University.The evaluation is well positioned to advance the evidence base surrounding contraceptive access initiatives, particularly given the uniqueness of CW in the southeastern United States.DESCRIPTION OF THE CHOOSE WELL INITIATIVE In 2017, the nonprofit organization New Morning (NM) 1 launched CW, a six-year statewide contraceptive access initiative.The mission of CW is to promote equitable access to contraception without judgment or coercion, aiming for a 25% reduction in statewide unintended pregnancy by 2023.CW is informed by collective impact principles as a means to centrally coordinate geographically distributed stakeholders.2 Its collective approach supports transformative change through ongoing communication among stakeholders, partner meetings and workgroups, and shared data collection standards.NM serves as the coordinating agency, managing all activities and funding all participating agencies.CW is unique and innovative in key ways.It is the first and only contraceptive access initiative of its kind in the US Southeast.CW's efforts are systematically coordinated across various clinical sectors (federally qualified health centers, hospital inpatient and outpatient providers, rural health clinics, free clinics, college and university health centers,
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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 | 0.124 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".