Abortion in a military population: A review of the literature
Bibliographic record
Abstract
Introduction: Abortion is recognized by the World Health Organization as a fundamental part of good reproductive care, and worldwide three of 10 pregnancies will end in abortion. In a military context, the number of women in the armed forces (AF) worldwide increases each year, with an average of 12.5% across North Atlantic Treaty Organization member states in 2021. Despite the increasing number of women in the AF, little is known about their abortion experiences, or even the prevalence. Methods: This systematic search of the literature was conducted in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. MEDLINE, CINAHL, and Google Scholar were searched for relevant articles, and bibliographies were screened. Qualitative themes were determined by two authors using iterative thematic analysis. Results: Twenty-three articles were identified; eight met the inclusion criteria. All but one focused on a U.S. population. Three themes emerged: rates of unintended pregnancy and abortion, accessibility to abortion care, and reasons for abortion. In the majority of studies, service women (SW) had an increased rate of unintended pregnancies but a lower abortion rate. SW face policy, logistical, and institutional barriers to accessing abortion, in addition to those faced by the civilian population. Discussion: Overall, there is a paucity of information on abortion in the AF worldwide. This review highlights the need for nation-specific research into SW's experience of abortion and provides guidance on how to do so to enhance understanding and lead to evidence-based national policy on abortion for AF personnel.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".