Synthesis of studies of co‐occurring disorder(s) in criminal justice and a research agenda
Bibliographic record
Abstract
The studies reported in this special issue were designed to take advantage of the unique opportunity that the Criminal Justice Drug Abuse Treatment Studies (CJDATS) cooperative provides to the systematic study of several key issues in programming for co-occurring disorder(s) (COD) in the criminal justice system. These papers present findings from CJDATS studies pertaining to co-occurring disorder(s), identify clinical initiatives to strengthen efforts to treat the population with co-occurring disorder(s), and point to a direction for the elaboration of a future research agenda. Four key areas of investigation are presented: Screening and Diagnosis; the Relationship of Co-Occurring Disorder(s) to Violence; Gender Differences; and the Delivery of Services for Co-Occurring Disorder(s). The first section of this article summarizes the studies included in this special issue within the context of the research literature already available. The second section suggests a future research agenda for the study of offender populations with co-occurring disorder(s), and concludes with a broad statement of clinical advancements to date.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".