Attempted Suicide: A Multilevel Examination of Inmate Characteristics and Prison Context
Bibliographic record
Abstract
Correctional institutions in the United States witness higher rates of suicide compared with the general population, as well as a higher number of attempted suicides compared with completed cases. Prison research focused little attention on investigating the combined effects of inmate characteristics and prison context on suicide, with studies using only one level of analysis (prison or prisoner) and neglecting the nested nature of inmates in prisons. To extend this literature, multilevel modeling techniques were employed to investigate individual- and prison-contextual predictive patterns of attempted suicide using a nationally representative sample of 18,185 inmates in 326 prisons across the United States. Results revealed that several individual-level factors predicted odds for attempted suicide, such as inmate characteristics/demographics, prison experiences, having a serious mental illness, and symptoms of mental health issues. Some prison-contextual variables, as well as cross-level interaction effects, also significantly predicted odds for attempted suicide. Policy and research implications are discussed.
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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 | 0.000 | 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) | 0.000 | 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".