A landscape of available data on contraceptive care in the United States
Bibliographic record
Abstract
OBJECTIVE: With this data landscape, we aim to (1) feature data sources that measure the dynamics of contraceptive care provision and (2) identify gaps in data availability. STUDY DESIGN: Through literature review and expert consultations, we identified data sources that describe the provision of contraceptive care in the United States. We highlight key features of each dataset, including the type of data collected, information on the sample and sampling approach, how the data are accessed, and an inventory of key indicators included about contraceptive care. RESULTS: We identified 29 relevant data sources - 16 provide individual-level data only, six provide systems-level data only, and seven provide both individual and systems-level data. Important gaps include a need for more robust collection and dissemination of systems-level data, stronger linkages between systems- and individual-level data, and more targeted data collection efforts on key subpopulations. CONCLUSIONS: The availability of ongoing high-quality data on key sexual and reproductive health metrics is crucial for holding policymakers and program planners accountable to meeting the needs of their most marginalized constituents or beneficiaries. This landscape may serve as a resource for researchers, program planners, and policymakers seeking to use data in their work. IMPLICATIONS: This landscape identifies key gaps in available data on contraceptive services in the U.S., including limited systems-level data and insufficient data on key subpopulations. Given the uncertainty of public resources for many of these datasets, additional funding resources will be needed to sustain and improve these data efforts going forward.
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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 | 0.057 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.031 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.007 |
| 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".