Internal consistency and concurrent validity of self-report components of a new instrument for the assessment of suicidality, the Suicide Ideation and Behavior Assessment Tool (SIBAT)
Bibliographic record
Abstract
This study aimed to assess the internal consistency of self-report components of the Suicide Ideation and Behavior Assessment Tool (SIBAT) and validate it with relevant elements of the Mini International Neuropsychiatric Interview (MINI). The SIBAT is a newly developed instrument for the evaluation of suicidality. In this study, we invited university students and trainees participating in a study of addictions to complete the self-report component of the SIBAT as an add-on study. We evaluated the internal consistency of the self-report component of the SIBAT and validated it against the suicidality component of the MINI. Data were analysed using both complete case analysis and multiple imputation. SIBAT data were collected for 394 participants, 314 of whom had also completed the MINI. The internal consistency of modules 2, 3, and 5 of the SIBAT was high. Each item from module 5 had a statistically significant association with the corresponding item from the MINI. The sum of scores from modules 2 and 3 had a moderate correlation with the assessment of suicide risk determined by the MINI, and a strong correlation with the total score of SIBAT module 5. The completion median time of modules 2, 3 and 5 was 14.3 min.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".