Rates, risk factors & methods of self harm among minority ethnic groups in the UK: a systematic review
Bibliographic record
Abstract
BACKGROUND: Studies suggest that the rates of self harm vary by ethnic group, but the evidence for variation in risk factors has not been synthesised to inform preventive initiatives. METHODS: We undertook a systematic literature review of research about self harm that compared at least two ethnic groups in the United Kingdom. RESULTS: 25 publications from 1765 titles and abstracts met our inclusion criteria. There was higher rate of self harm among South Asian women, compared with South Asian men and White women. In a pooled estimate from two studies, compared to their white counterparts, Asian women were more likely to self harm (Relative Risk 1.4, 95%CI: 1.1 to 1.8, p = 0.005), and Asian men were less likely to self harm (RR 0.5, 95% CI: 0.4 to 0.7, p < 0.001). Some studies concluded that South Asian adults self-harm impulsively in response to life events rather than in association with a psychiatric illness. Studies of adolescents showed similar methods of self harm and interpersonal disputes with parents and friends across ethnic groups. There were few studies of people of Caribbean, African and other minority ethnic groups, few studies took a population based and prospective design and few investigated self harm among prisoners, asylum seekers and refugees. CONCLUSION: This review finds some ethnic differences in the nature and presentation of self harm. This argues for ethnic specific preventive actions. However, the literature does not comprehensively cover the UK's diverse ethnic groups.
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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 | 0.017 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".