Predictive Validity of the <scp>MINI</scp> Suicidality Subscale for Suicide Attempts in a Homeless Population With Mental Illness
Bibliographic record
Abstract
OBJECTIVE: Suicide is a leading cause of death, yet the accurate prediction of suicidal behavior is an elusive target for clinicians and researchers. The current paper examines the predictive validity of the Mini Neuropsychiatric Interview (MINI) Suicidality subscale for suicide attempts (SAs) for a homeless population with mental illness. METHODS: Two thousand two hundred and fifty-five homeless individuals with mental illness across five Canadian cities enrolled in the At Home/Chez Soi Housing First trial interviewed at baseline using the MINI Suicidality subscale with 2-year follow-up of self-reported SAs. RESULTS: Two thousand two hundred and twenty-one participants were included in the analysis. High rates of mood and substance use disorders were present (56.5% and 67.4%, respectively). The mean MINI Suicidality subscale score was 7.71. Among 1,700 participants with follow-up data, 11.4% reported a SA over the 2-year study period. MINI Suicidality subscale scores were predictive of SAs (AUC ≥ 0.70) among those with and without a history of SAs, even among those with missing answers. A positive predictive value of 0.20 and a negative predictive value of 0.95 were demonstrated, with a relatively low number needed to assess of 4.5-5. CONCLUSION: The MINI Suicidal subscale shows promise as an easy to use and accurate suicide risk prediction tool among homeless individuals with mental illness.
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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) | 0.001 | 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) | 0.000 | 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".