Preventative efforts surrounding inmate suicide
Bibliographic record
Abstract
The overall purpose of this study was to gain a deeper understanding of inmate suicide and preventative methods that are practiced among correctional facilities. This study discusses contributing factors of inmate suicide, risk factors among inmates, suicide preventative methods, and 4 preventative programs that were carried out in different countries. The research design used in this paper was a case study approach where the researcher gained access to 4 publicly available studies conducted in correctional facilities. The research found that there are multiple factors that play a role in inmate suicide from victim demographics to location of where the suicidal act was committed. Preventative methods that have been widely utilized includes proper staff training, accurate intake and screening assessments, appropriate housing placement of inmates, constant observation and monitoring, and effective communication. The cases that were studied are as follows: the Depression Hopelessness and Suicide (DHS) screening form, Skills-Based Training on Risk Management (STORM) training, Samaritans of Southern Alberta (SAMS), and the Inmate Observer Program (IOP). Each preventative program has its own merits; however, two of the programs seemed to stand out above the others as being most effective. The author believes that if correctional facilities implement proper preventative efforts and skillful techniques, suicide among inmates could likely be prevented.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".