An ethical paradox: Addressing both disparities in access to evidence-based treatment and the use of coercive practices for individuals with severe mental illness
Bibliographic record
Abstract
Individuals with severe mental illness (SMI), including schizophrenia, bipolar disorder and major depressive disorder, face a significant paradox within healthcare systems. They are frequently underserved in the provision of evidence-based medical treatments, leading to poorer health outcomes. Yet, they are also disproportionately subjected to coercive psychiatric practices that lack robust evidence of effectiveness and may inflict harm. This paper explores the ethical implications and consequences for public health, drawing upon recent research including both systematic reviews and epidemiological studies, to highlight the need for change to address these ethical challenges. This is vital to good public health practice as equity and inequity are core public health issues in vulnerable populations such as those with SMI.
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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 | 0.167 | 0.370 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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".