Where Do Reproductive-Aged Women Want to Get Contraception?
Bibliographic record
Abstract
Background: People's preferences regarding how they want to obtain contraception should be considered when building and refining high-quality contraceptive care programs, especially in light of recent shifts to incorporate more telehealth options into contraceptive care due to the coronavirus disease 2019 (COVID-19) pandemic. Methods: Our study is a cross-sectional analysis of population-representative surveys conducted between November 2019 and August 2020 among women aged 18–44 years in Arizona ( N = 885), New Jersey ( N = 952), and Wisconsin ( N = 967). We use multivariable logistic regression to identify characteristics associated with each of five contraception source preference groups (in-person via health care provider, offsite with a provider via telemedicine, offsite without a provider via telehealth, at a pharmacy, or via innovative strategies), and we examine associations between contraceptive care experiences and perceptions and each preference group. Results: Across states, most respondents (73%) expressed preferences for obtaining contraception via more than one source. One quarter indicated a narrow preference for obtaining contraception in-person from a provider, 19% expressed interest in doing so offsite with a provider via telemedicine, 64% for doing so offsite without a provider via telehealth, 71% reported interest in pharmacy-based contraception, and 25% indicated interest in getting contraception through innovative strategies. Those who had experienced nonperson-centered contraceptive counseling reported higher levels of interest in telehealth and innovative sources, and those who expressed mistrust in the contraceptive care system had higher levels of preferring to obtain contraception offsite, via telemedicine, telehealth, and other innovative avenues. Conclusions: Policies that ensure access to a diversity of contraceptive sources, which acknowledge and address people's past experiences of contraceptive care, have the greatest likelihood of closing the gap between people's contraceptive access preferences and realities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".