Assessing the risk for suicide in schizophrenia according to migration, ethnicity and geographical ancestry
Bibliographic record
Abstract
BACKGROUND: Suicide is a leading cause of mortality among those afflicted by schizophrenia. Previous studies demonstrated that the stressors associated with immigration may lead to an onset of schizophrenia and suicide separately in susceptible individuals. However, no studies have shown whether immigration may lead to suicidal behaviour for individuals with schizophrenia. Our study proposes that an individual's geographical ancestry, ethnicity or migration status may be predictive of suicide risk in schizophrenia. METHODS: In a sample of 276 participants with schizophrenia spectrum disorders, we conducted cross-sectional assessments to collect clinical information. Self-identified ethnicity and suicide history were collected through self-report questionnaires and interview-based scales. Ancestry was identified using 292 genetic markers from HapMap. Migrants were classified as those who immigrated to Canada during their lifetime. Using a regression analysis, we tested whether a history of migration, ethnicity or geographical ancestry were predictive of a history of suicide attempts. RESULTS: Our analysis failed to demonstrate a significant relationship between suicide history and migration, ethnicity or ancestry. However, ethnicity appears to be significantly associated with the number of psychiatric hospitalizations in our sample. CONCLUSION: Ethnicity and migration history are not predictive of previous suicide attempts. Ethnicity may be an important demographic factor affecting access to mental health resources and frequency of hospitalizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".