Bibliographic record
Abstract
Although unintended childbearing has declined in recent years (Finer and Zolna, 2016; Jones and Jerman, 2017), reducing unintended childbearing remains a public health goal in the U.S. due to its links to poorer outcomes for mothers, children, and families (Healthy People 2030).In this profile, we investigate trends in birth intendedness among women 15-44 between 1997 and 2018 using the 2002, 2006-10, 2011-15, and 2015-19 cycles of the National Survey of Family Growth 1 .Birth intendedness is based on a series of questions in which women were asked to characterize each birth as on time, mistimed (wanted but occurring earlier than desired), or unwanted (the respondent did not want any births at all or no additional births).When births were reported as mistimed, women were asked how much earlier than desired the birth occurred, and we categorize mistimed births into two groups: slightly mistimed (less than two years earlier than desired) or seriously mistimed (two or more years too early).This profile is an update of FP-17-08 and is the first in a three-part series on unintended fertility in the U.S.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".