Mental health conditions in bereaved military service widows: A prospective, case‐controlled, and longitudinal study
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Bereavement is associated with increases in prevalence of mental health conditions and in healthcare utilization. Due to younger age and bereavement by sudden and violent deaths, military widows may be vulnerable to poor outcomes. No systematic research has examined these effects. METHOD: Using outpatient medical records from wives of active-duty military service members (SMs), we compared the prevalence of mental health conditions and mental healthcare visits among case widows (n = 1,375) to matched (on age, baseline healthcare utilization, SM deployment, and rank) nonbereaved control military wives (n = 1,375), from 1 year prior (Yr-1) to 2 years following (Yr+1 and Yr+2) SM death. Prevalence risk ratios and confidence intervals were compared to determine prevalence rates of mental health conditions and outpatient mental healthcare visits over time. RESULTS: The prevalence of any mental health condition, as well as a distinct loss- and stress-related mental health conditions, significantly increased from Yr-1 to Yr+1 and Yr+2 for cases as did mental healthcare utilization. Widows with persistent disorders (from Yr+1 to Yr+2) exhibited more mental conditions and mental healthcare utilization than widows whose conditions remitted. CONCLUSION: Bereavement among military widows was associated with a two- to fivefold increase in the prevalence of depression, posttraumatic stress disorder, and adjustment disorder postdeath, as well as an increase in mental healthcare utilization. An increase in the prevalence of loss- and stress-related conditions beyond 1 year after death indicates persistent loss-related morbidity. Findings indicate the need for access to healthcare services that can properly identify and treat these loss-related conditions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".