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Record W2097909510 · doi:10.1192/bjp.bp.107.042598

Suicide rates in people of South Asian origin in England and Wales: 1993–2003

2008· article· en· W2097909510 on OpenAlexaff
Kwame McKenzie, Kamaldeep Bhui, Kiran Nanchahal, Bob Blizard

Bibliographic record

VenueThe British Journal of Psychiatry · 2008
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsDiasporaDemographyEthnic groupSuicide ratesSuicide preventionSouth asiaGeographyPopulationMedicineInjury preventionNew englandPoison controlGerontologyHistoryGender studiesMedical emergencyEthnologySociologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Low rates of suicide in older men and high rates in young women have been reported in the South Asian diaspora worldwide. Calculating such suicide rates in the UK is difficult because ethnicity is not recorded on death certificates. AIMS: To calculate the South Asian origin population suicide rates and to assess changes over time using new technology. METHOD: Suicide rates in England and Wales were calculated using the South Asian Name and Group Recognition Algorithm (SANGRA) computer software. RESULTS: The age-standardised suicide rate for men of South Asian origin was lower than other men in England and Wales, and the rate for women of South Asian origin was marginally raised. In aggregated data for 1999-2003 the age-specific suicide rate in young women of South Asian origin was lower than that for women in England and Wales. The suicide rate in those over 65 years was double that of England and Wales. CONCLUSIONS: Older, rather than younger, women of South Asian origin seem to be an at-risk group. Further research should investigate the reasons for these changes and whether these patterns are true for all South Asian origin groups.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.997

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)0.0000.000

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 head0.022
GPT teacher head0.286
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations75
Published2008
Admission routes1
Has abstractyes

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