Barriers to and facilitators of military spouses’ recovery from perinatal mental health disorders: A qualitative study
Bibliographic record
Abstract
Introduction: Perinatal mental health disorders (PMHDs) are a common complication of child-bearing, affecting approximately 1 in 7 U.S. mothers. An expanding literature has examined how PMHDs affect military families; however, little is known about military spouses' experiences in accessing and engaging in treatment for PMHDs. The purpose of this qualitative study was to gain a better understanding of the barriers to and facilitators of accessing, engaging, and progressing in treatment and recovery among a sample of U.S. military spouses with PMHDs. Methods: Military spouses (N = 12) were recruited from a maternal mental health clinic at an academic medical centre in San Diego, California, United States. Five semi-structured focus groups were recorded, transcribed verbatim, and analyzed by research team members until consensus on themes was reached. Results: Eight themes emerged: five main barriers (stigma, impacts on service member's career, lack of support, accessibility, practical and logistical concerns) and three main facilitators (solid support structure, encouragement to seek help, practical and logistical facilitators). Discussion: Findings enhance and complement extant research examining barriers to mental health care treatment and recovery among military spouses and suggest barriers to and facilitators of PMHDs. Fear of harming the serving spouse's career can be mitigated through supportive military leadership advocating for serving spouses to support their partners' recovery. Education for military leaders in foundational knowledge of PMHDs, including screening, treatment, stigmas, and impact on families, is needed to create supportive and encouraging environments leading to open dialogue and non-punitive solutions that facilitate military spouses' recovery from PMHDs.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".