Pregnancies and live births, active component service women, U.S. Armed Forces, 2012-2016.
Bibliographic record
Abstract
This report summarizes data on the demographic and military characteristics of women and women of childbearing potential (WOCBP) in the active component of the U.S. Armed Forces during 2012-2016. Data on pregnancy-related care and birth rates are also presented. In 2016, WOCBP comprised the vast majority of active component service women. The largest proportions of WOCBP were in the categories of women who were 20-24 years old, non-Hispanic white, junior enlisted rank, and in a communications/intelligence occupation. WOCBP were roughly equally distributed in the Army, Navy, and Air Force, whereas only 7.5% served in the Marine Corps. Slightly more than one-quarter of WOCBP had ever deployed to the U.S. Central Command area of responsibility (CENTCOM AOR). In 2016, 13.1% of all WOCBP had at least one pregnancy-related event and 1.1% of deployed WOCBP had a pregnancy event during a deployment to CENTCOM AOR. The prevalence of pregnancy decreased slightly over the surveillance period. There were 63,879 live births during the surveillance period, for an overall live birth rate of 64.9 live births per 1,000 person-years (p-yrs). This rate of live births decreased steadily from 69.8 per 1,000 p-yrs in 2012 to 59.7 per 1,000 p-yrs in 2016. Rates of live births were highest among women who were 30-34 years old, enlisted or junior officer rank, Army, in healthcare occupations, and married. About one-quarter of the total live births were cesarean deliveries.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".