Do stigma and other perceived barriers to mental health care differ across Armed Forces?
Bibliographic record
Abstract
OBJECTIVES: Military organizations are keen to address barriers to mental health care yet stigma and barriers to care remain little understood, especially potential cultural differences between Armed Forces. The aim of this study was to compare data collected by the US, UK, Australian, New Zealand and Canadian militaries using Hoge et al.'s perceived stigma and barriers to care measure (Combat duty in Iraq and Afghanistan, mental health problems and barriers to care. New Engl J Med 2004;351:13-22). DESIGN: Each member country identified data sources that had enquired about Hoge et al.'s perceived stigma and perceived barriers to care items in the re-deployment or immediate post-deployment period. Five relevant statements were included in the study. SETTING: US, UK Australian, New Zealand and Canadian Armed Forces. RESULTS: Concerns about stigma and barriers to care tended to be more prominent among personnel who met criteria for a mental health problem. The pattern of reported stigma and barriers to care was similar across the Armed Forces of all five nations. CONCLUSIONS: Barriers to care continue to be a major issue for service personnel within Western military forces. Although there are policy, procedural and cultural differences between Armed Forces, the nations studied appear to share some similarities in terms of perceived stigma and barriers to psychological care. Further research to understand patterns of reporting and subgroup differences is required.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".