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Record W2155864676 · doi:10.1258/jrsm.2010.090426

Do stigma and other perceived barriers to mental health care differ across Armed Forces?

2010· article· en· W2155864676 on OpenAlexaffabout
Matthew Gould, Amy B. Adler, Mark A. Zamorski, Carl A. Castro, Natalie Hanily, Nicole M. Steele, Steve Kearney, Neil Greenberg

Bibliographic record

VenueJournal of the Royal Society of Medicine · 2010
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsStigma (botany)Social stigmaMental healthPsychiatryPrejudice (legal term)MedicinePsychologyData scienceSocial psychologyComputer scienceFamily medicineHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.380
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations162
Published2010
Admission routes2
Has abstractyes

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