A Supplemental Women’s Health Questionnaire for Women Veterans With Military Environmental Exposures: Project Development and Implementation
Bibliographic record
Abstract
Background: The number of women in the armed forces has steadily increased across all branches, even as the overall size of the military remains stable. The population of women veterans is also expanding. The existing literature has extensively reported the impact of military environmental exposures (MEEs) on adverse physical and mental health outcomes in service members and veterans; however, most of these studies focus on the experiences of men. In response to the growing need to address women-specific health care concerns, particularly for women with MEEs, the Women's Operational Military Exposure Network Center of Excellence (WOMEN CoE) developed and implemented the Women's Health Addendum (WHA). Objective: The primary objective of this project is to (1) describe the development and implementation of a comprehensive health questionnaire for women veterans, (2) systematically describe and characterize the health conditions of women seeking care for MEE-related health concerns, and (3) use findings to inform clinic policies and develop targeted programs. Methods: The WHA was introduced to assess the prevalence of health conditions that are female-specific, or disproportionately impact women; examine the relationship between these health conditions and MEEs; and use findings to improve care. The WHA was developed through an iterative process, incorporating literature review, veteran and clinician feedback, and clinical expertise. It consists of 81 questions across 7 categories related to health conditions across the lifespan and was implemented in 2 phases. Phase 1 was administered to women at the California War Related Illness and Injury Study Center (WRIISC), and phase 2 included women at the New Jersey and Washington, DC, WRIISC sites. Descriptive findings are presented here. Results: A total of 63 women participated in the program evaluation from October 2022 to April 2024. In phase 1, 39% (29/75) of the women who were invited agreed to participate. In phase 2, 34 (10%) of the 325 invited veterans responded. Several women's health conditions were reported, with approximately 97% (61/63) of women reporting at least one health condition and 87% (55/63) reporting 3 or more. Among respondents, the most prevalent conditions included sexual dysfunction (23/33, 70%), urinary incontinence (33/56, 59%), pelvic floor dysfunction (33/63, 52%), and pregnancy loss (20/45, 44%). Overall, more than 40% (3/7) of the most frequent conditions were related to urinary health and pelvic floor dysfunction. Conclusions: Findings highlight the need for services related to women's health, especially for this cohort with MEE concerns seen at a tertiary care center. Initial findings emphasize concerns that women have about fertility and MEE experienced during deployments. Next steps include administering the WHA to women at sister WRIISC sites in real time and establishing a wider distribution network for the WHA. Future efforts to further evaluate the relationship between MEE and women's health concerns are underway.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".