Changes in Mental Health Policy and Their Influence on Self-Injurious Behaviours in the Israeli Military Prison System
Bibliographic record
Abstract
OBJECTIVE: Self-injurious behaviours (SIBs) are widespread among prisoners and are sometimes classified as potentially calculated acts intended to control others. Access to a psychiatrist may be valued by imprisoned soldiers seeking immediate release from service. The main goal of the study was to assess the effects of a new mental health regulation in the Israel Defense Forces (IDF) prison system, which requires ordering an immediate interview with a psychiatrist for inmates performing SIBs. METHOD: Frequency of SIBs in 2 military prisons was examined and compared for 11 months before and 10 months after the implementation of the regulation. Severity of injuries and rates of release from service were examined for 4 months. RESULTS: The frequency of SIBs significantly increased after the implementation of the regulation in one prison, but not in the other. No effect was found for rate of release from service or for severity of injuries. CONCLUSIONS: Increased accessibility to a psychiatrist for military prisoners may have resulted in increased motivation for inmates to perform SIBs. These effects are limited to the prison that serves a higher proportion of highly stressed soldiers.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".