Self-harm in British South Asian women: psychosocial correlates and strategies for prevention
Bibliographic record
Abstract
OBJECTIVE: To review the rates of self-harm in British South Asian women, look into the factors that contribute to these high rates of self-harm and discuss possible strategies for prevention and provision of culturally sensitive service for South Asian women who harm themselves. METHOD: Review. RESULTS: South Asian women are significantly more likely to self harm between ages 16-24 years than white women. Across all age groups the rates of self harm are lower in South Asian men as compared to South Asian women. These women are generally younger, likely to be married and less likely to be unemployed or use alcohol or other drugs. They report more relationship problems within the family. South Asian women are less likely to attend the ER with repeat episode since they hold the view that mainstream services do not meet their needs. CONCLUSION: South Asian women are at an increased risk of self harm. Their demographic characteristics, precipitating factors and clinical management are different than whites. There is an urgent need for all those concerned with the mental health services for ethnic minorities to take positive action and eradicate the barriers that prevent British South Asians from seeking help. There is a need to move away from stereotypes and overgeneralisations and start from the user's frame of reference, taking into account family dynamics, belief systems and cultural constraints.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".