Mental health treatment patterns following screening at intake to prison.
Bibliographic record
Abstract
OBJECTIVE: While there is general consensus about the need to increase access to mental health treatment, it is debated whether screening is an effective solution. We examined treatment use by inmates in a prison system that offers universal mental health screening. METHOD: We conducted an observational study of 7,965 consecutive admissions to Canadian prisons. We described patterns of mental health treatment from admission until first release, death, or March, 2015 (median 14-month follow-up). We explored the association between screening results and time of first treatment contact duration of first treatment episode, and total number of treatment episodes. RESULTS: Forty-three percent of inmates received at least some treatment, although this was often of short duration; 8% received treatment for at least half of their incarceration. Screening results were predictive of initiation of treatment and recurrent episodes, with stronger associations among those who did not report a history prior to incarceration. Half of all inmates with a known mental health need prior to incarceration had at least 1 interruption in care, and only 46% of inmates with a diagnosable mental illness received treatment for more than 10% of their incarceration. CONCLUSION: Screening results were associated with treatment use during incarceration. However, mental health screening may have diverted resources from the already known highest need cases toward newly identified cases who often received brief treatment suggestive of lower needs. Further work is needed to determine the most cost-effective responses to positive screens, or alternatives to screening that increase uptake of services. (PsycINFO Database Record
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".