Suicide by burning in the South Asian origin population in England and Wales a secondary analysis of a national data set
Bibliographic record
Abstract
Objectives A descriptive analysis of suicide by burning in England and Wales in the general population and in people of South Asian origin. Design A cross-sectional secondary analysis of a national data set. Setting A population study of all those who died by suicide in England and Wales between 1993 and 2003 inclusive. Participants All cases of suicide and undetermined intent identified by the Office for National Statistics for England and Wales. A computer algorithm was used to identify people of the South Asian origin from their names. There were 55 140 suicides in the UK between 1993 and 2003. The ratio of male to female suicides was 3:1. There were 1455 South Asian suicides identified by South Asian Name and Group Recognition Algorithm. Primary and secondary outcome measures Death by suicide and undetermined intent, as determined by Coroner's Inquest. ICD9 codes E958.1 and E988.1 and ICD10 codes X76 and Y26. Results 1.77% of suicides in the general population and 8.45% of suicides in the South Asian origin population were by burning. The suicide rate by burning was 0.8/100 000 person-years for England and Wales and 2.9/100 000 person-years for the South Asian origin population. The odds of suicide by burning were increased in the South Asian group as a whole (OR 3.06, 95% CI 2.30 to 4.08). Those born in Asia and Africa were at higher risk than those born in the UK (OR 2.69, 95% CI 2.01 to 3.60 and OR 2.10, 95% CI 1.46 to 3.01, respectively). The increased risk was for those aged 25-64 years. Conclusion Suicide by burning remains a significant issue in the South Asian origin working-age population in England and Wales. A prevention strategy could target working-age people of South Asian origin born abroad as they are at the highest risk. More in depth research on the reasons for using this method may help to identify possible prevention strategies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".