Suicidal ideation across race in a justice‐involved sample: An item response theory approach
Bibliographic record
Abstract
OBJECTIVES: Compared to community samples, rates of suicide are much higher in forensic and correctional settings, yet limited research has focused on the development and improvement of suicide assessment methods used in such contexts. Moreover, despite evidence that suicide assessment varies across Caucasians and African Americans, to our knowledge this important issue has received little attention within higher risk correctional samples. We used Item Response Theory and Differential Item Functioning analyses to address this gap within the literature. METHOD: Specifically, we examined the psychometric properties of the Suicidal Ideation scale of the Personality Assessment Inventory (Morey, 2007) in a large sample of justice-involved individuals. RESULTS: Caucasians report greater suicidal ideation compared to African American participants on average. However, after controlling for mean differences, Caucasians and African Americans differentially endorsed symptoms of suicidal ideation. If the level of suicidal ideation is held constant across racial categories, Caucasians underreported suicidal ideation relative to African Americans. CONCLUSION: Results suggest a nuanced picture of suicidal ideation across racial categories that can be informed by Item Response Theory approaches to scale construction and refinement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".