Evaluating the Sociocultural Risks for Suicide Attempt in Schizophrenia
Bibliographic record
Abstract
Sociocultural factors involved in immigration and ethnicity are believed to independently influence the risk for schizophrenia and suicidal behaviour. Schizophrenia patients are known to be at an increased risk for suicide. Though many risk factors have been identified, few have any significant clinical impact on predicting suicide. Whether immigration and ethnicity are potential risk factors for suicide attempt in schizophrenia has yet to be investigated. Schizophrenia patients with clear suicide attempt history, immigration history, self-reported ethnicity, and other sociocultural and clinical variables were recruited to test whether independently, or synthesized in a classification algorithm, these variables can accurately predict a history of suicide attempt. Both immigration and ethnicity had a non-significant association with suicide attempt. Inclusion of these predictor variables also did little to improve classification accuracy in our algorithms. Taken together, we found no evidence that these sociocultural factors have a significant influence on the risk for suicide attempt in schizophrenia.
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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 | 0.000 | 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 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".