Depression prevalence and geographic distribution in United States military women: results from the 2017 Service Women’s Action Network needs assessment
Bibliographic record
Abstract
Introduction:To better understand depression in United States (US) servicewomen, needs assessment data from the Service Women’s Action Network (SWAN) were collected and analyzed, with comparison samples drawn from the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS). The purpose of the present study was threefold. First, an assessment of the spatial distribution of depression in the United States among military women was made using geographic information systems. Second, the authors sought to determine differences in the prevalence of undiagnosed mental health concerns and diagnosed depression in women by military service status. Third, the authors sought to identify risk factors for depression among military women. Methods: Frequencies and percentages for all demographic, geographic, and outcome variables were calculated by military service status and data source. Differences among three groups – non-Veteran respondents of the BRFSS, Veteran respondents of the BRFSS, and SWAN member Veterans – were analyzed with the Chi-square test of independence. Estimates of the state-level prevalence of undiagnosed mental health concerns and diagnosed depression among military women who responded to either the 2016 BRFSS or the 2017 SWAN needs assessment were calculated and represented with state-boundary choropleth maps in Quantum GIS (QGIS). Results: A multinomial logistic regression model, adjusted for educational attainment, race, ethnicity, employment status, US region, and rurality, showed that military women and women Veterans were more likely to have undiagnosed mental health concerns and diagnosed depression, χ2 28 = 4,891.91, p < 0.001, Nagelkerke’s R2 = 0.03. Spatial analysis indicated that respondents living in the South were more likely to have diagnosed depression or undiagnosed mental health symptoms in both the BRFSS and SWAN needs assessment samples. Discussion: Primary findings from this study suggest that given the regional variation in depression among women Veterans, future studies should work to examine the role of the region in mental health for servicewomen in the United States, looking at available services and cultural differences. Recommendations include targeted programming for women Veterans.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".