Diagnostic overshadowing: A cause of overlooked depression in prisoners
Bibliographic record
Abstract
Depression is a pervasive and prevalent mental health disorder among prisoners, yet it frequently remains undiagnosed due to the phenomenon of diagnostic overshadowing - a clinical bias where prominent conditions or contexts obscure less obvious mental health issues. This review explores diagnostic overshadowing as a key barrier to identifying depression in people in custody, focusing on diagnostic failures, their consequences, and solutions. We review global literature on depression prevalence, which affects prisoners at significantly higher rates than the general population, yet it is frequently misdiagnosed or goes unrecognized due to overshadowing by the more prominent features of substance use, challenging behaviours or the prison context itself. Failure to recognize readily treatable depressive disorders because of diagnostic overshadowing results in serious but avoidable consequences, including elevated suicide rates, increased recidivism, and other adverse outcomes such as substance abuse and institutional violence, which collectively and individually perpetuate the cycle of personal distress and dysfunction, as well as the human and associated costs to others in society. By describing and highlighting diagnostic overshadowing as a particular problem in correctional settings, and by exploring its mechanisms in correctional psychiatry settings, we advocate for solutions for more reliable recognition of depression in prisoners, which is an essential first step to effectively address this readily treatable but easily overlooked serious mental disorder.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".