Use of Health Insurance Among Clients Seeking Contraceptive Services at Title X–Funded Facilities in 2016
Bibliographic record
Abstract
CONTEXT: As federal initiatives aim to fundamentally alter or dismantle the Affordable Care Act (ACA), evidence regarding the use of insurance among clients obtaining contraceptive care at Title X-funded facilities under ACA guidelines is essential to understanding what is at stake. METHODS: A nationally representative sample of 2,911 clients seeking contraceptive care at 43 Title X-funded sites in 2016 completed a survey assessing their characteristics and insurance coverage and use. Chi-square tests for independence with adjustments for the sampling design were conducted to determine differences in insurance coverage and use across demographic characteristics and facility types. RESULTS: Most clients (71%) had some form of public or private health insurance, and most of these (83%) planned to use it to pay for their services. Foreign-born clients were less likely than U.S.-born clients to have coverage (46% vs. 75%) and to use it (78% vs. 85%). Clients with private insurance were less likely than those with public insurance to plan to use their insurance (75% vs. 91%). More than one-quarter of clients not planning to use existing insurance for services indicated that the reason was that someone might find out. CONCLUSION: Coverage gaps persist among individuals seeking contraceptive care within the Title X network, despite evidence indicating increases in health insurance coverage among this population since implementation of the ACA. Future research should explore the impact of altering or eliminating the ACA both on the Title X provider network and on the individuals who rely on it.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".