Women Inmates' Mental Health Needs: Evidence of the Validity of the Jail Screening Assessment Tool (JSAT)
Bibliographic record
Abstract
In British Columbia, Canada, the challenge of caring for mentally disordered inmates in jails has been met with a two-tiered assessment approach: screening followed by comprehensive assessments. Intake interviewers evaluate mental disorder and risk for violence, suicide, self-harm, and victimization. The screening and risk management procedures were published in the Jail Screening Assessment Tool (JSAT). The results of two prospective studies of mental health screening with the JSAT in two samples of women inmates are presented. The first study reports the prevalence of mental health needs among female inmates based on results from JSAT and the Brief Psychiatric Rating Scale-Expanded (BPRS-E). The second study tested the validity of referrals to the mental health program based on ∼20 min. semi-structured intake interviews using the JSAT (includes the BPRS-E) compared to independent evaluations of mental disorder with the Structured Clinical Interview for DSM-IV Non-Patient Edition (SCID-I/NP). Results of both studies indicated a high rate of substance abuse and other serious mental illnesses among female inmates. In the second study, intake interviewers using the JSAT were in agreement with independent SCID assessments at a rate significantly better than chance; yielding a sensitivity of 70.6% and a specificity of 75.0%. These preliminary results suggest the JSAT is a potentially effective tool for identifying female inmates in need of mental health services and specialized placement.
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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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".