Experiences of ethnic minority personnel in the armed forces: A systematic review
Bibliographic record
Abstract
Introduction: Ethnic minority personnel experience greater levels of harassment and discrimination than their non-minority counterparts. This review demonstrates the impact of these experiences on ethnic minority personnel in the armed forces. Methods: A literature search was conducted in PubMed, PsycInfo, PsycArticles, EBSCO, and Web of Science. Sixteen articles that discussed Black, Asian, and ethnic minority armed forces personnel were analyzed. Results: Much of what is known about ethnic minority experiences of serving in the armed forces is based on ethnic minorities in the U.S. Armed Forces. The available literature shows that ethnic minority serving personnel and Veterans experience greater disadvantage than their native counterparts, both during and after service. Ethnic minority personnel reported poorer health than white personnel and fear of criticism from their ethnic minority community on disclosure of traumatic experiences. Ethnic minority personnel were also more likely to access formal mental health services yet less likely to engage in treatment, particularly women. Three themes were identified: cultural identity, health status and health utilization, and trauma and discrimination. Discussion: Research reports often do not highlight individual ethnic minority groups, thus making it difficult to draw conclusions about them. Future research should consider evaluating the psychosocial context influencing functioning among different ethnic minority groups and should also explore the benefits of serving in the armed forces.
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 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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".