Design and Implementation of the Surveys of Women: Protocol for an Address-Based Sampling Multimodal Study
Bibliographic record
Abstract
BACKGROUND: Studies conducted in the United States such as the National Survey of Family Growth (NSFG) and the Pregnancy Risk Assessment Monitoring System (PRAMS) collect data on pregnancy intentions to aid in improving health education, services, and programs. PRAMS collects data from specific sites, and NSFG is a national household-based survey. Like NSFG, the Surveys of Women was designed to survey participants residing in households using an address-based sample and a multimode data collection approach. The Surveys of Women collects data from eligible participants in 9 states within the United States on contraception use, reproductive health, and pregnancy intentions. In this paper, we focus on the baseline data collection protocol, including sample design, data collection procedures, and data processing. We also include a brief discussion on the follow-up and endline survey methodologies. Our goal is to inform other researchers on methods to consider when fielding a household-level reproductive health survey. OBJECTIVE: The Surveys of Women was developed to support state-specific research and evaluation projects, with an overall goal of understanding contraceptive health practices among women aged 18-44 years. The project collects data from respondents in 9 different states (Arizona, Alabama, Delaware, Iowa, Maryland, New Jersey, Ohio, South Carolina, and Wisconsin) over multiple rounds. METHODS: Households were selected at random using address-based sampling methods. This project includes a cross-sectional baseline survey, 2 or 3 follow-up surveys with an opt-in panel of respondents, and a cross-sectional endline survey. Each round of data collection uses a multimode design through the use of a programmed web survey and a formatted hard copy questionnaire. Participants from the randomly selected households access their personalized surveys through a web survey or mail in a hard copy questionnaire. To maximize responses, these surveys follow a rigorous schedule of various prompts bolstering the survey implementation design, and the participants received a modest monetary incentive. RESULTS: This is an ongoing project with results published separately by the evaluation teams involved with data analysis. CONCLUSIONS: The methods used in the first baseline survey informed modifications to the methods used in subsequent statewide surveys. Data collected from this project will provide insight into women's reproductive health, contraceptive use, and abortion attitudes in the 9 selected states. The long-term goal of the project is to use a data collection methodology that collects data from a representative sample of participants to assess changes in reproductive health behaviors over time. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40675.
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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 | 0.098 | 0.094 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.083 | 0.026 |
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".