Yield and Efficiency of Mental Health Screening: A Comparison of Screening Protocols at Intake to Prison
Bibliographic record
Abstract
BACKGROUND: The value of screening for mental illness has increasingly been questioned in low prevalence settings due to high false positive rates. However, since false positive rates are related to prevalence, screening may be more effective in higher prevalence settings, including correctional institutions. We compared the yield (i.e. newly detected cases) and efficiency (i.e. false positives) of five screening protocols to detect mental illness in prisons against the use of mental health history taking (the prior approach to detecting mental illness). METHODS AND FINDINGS: We estimated the accuracy of the six approaches to detect an Axis I disorder among a sample of 467 newly admitted male inmates (83.1% participation rate). Mental health history taking identified only 41.0% (95% CI 32.1, 50.6) of all inmates with mental illness. Screening protocols identified between 61.9 and 85.7% of all cases, but referred between 2 and 3 additional individuals who did not have a mental illness for every additional case detected compared to the mental health history taking approach. In low prevalence settings (i.e. 10% or less) the screening protocols would have had between 4.6 and 16.2 false positives per true positive. CONCLUSIONS: While screening may not be practical in low prevalence settings, it may be beneficial in jails and prisons where the prevalence of mental illness is higher. Further consideration of the context in which screening is being implemented, and of the impacts of policies and clinical practices on the benefits and harms of screening is needed to determine the effectiveness of screening in these settings.
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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 | 0.058 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".