Evaluation of a Suicide Risk Assessment Tool in a Large Sample of Detained Youth.
Bibliographic record
Abstract
OBJECTIVES: We evaluated the six-item Inmate Security Assessment (ISA) tool used among detained youth in Manitoba, Canada. METHOD: Two hundred and forty-one recorded self-harm incidents among all incarcerated youth occurred between January 1, 2005 and December 31, 2010 (N=5102). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR) and negative likelihood ratio (NLR) for three categories of suicide risk (high, medium, and low) as well as each of the six suicide risk evaluation indicators were determined. Receiver operating characteristic (ROC) curves and area under the curve (AUC) calculations for the three suicide risk levels and the six indicators were created. RESULTS: Having at least a low suicide risk level (93.8%) or at least one suicide risk factor (94.6%) provided high sensitivity. Specificity was high if an individual had at least a medium suicide risk level (94.2%) or at least three suicide risk indicators (96.7%). The PPV was low (8.9-16.2%) and the NPV was high (94.9-99.3%) for all suicide risk levels. The most sensitive risk factor for self-harm was a prior history of suicidal behavior or a family history of suicide (94.6%). All risk indicators had a low PPV (7.4-23.1%) and a high NPV (95.4-99.5%). A very low NLR was found for those without prior suicidal behavior or a family history of suicide (0.107). The AUC was 0.719 (95%CI = 0.692-0.746), indicating a fair test. CONCLUSION: The ISA is a moderately accurate tool for identifying risk for self-harm in detained youth.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".