Bibliographic record
Abstract
Many psychiatrists have endorsed the idea of evidence-based psychiatry, the application of the principles of evidence-based medicine (EBM) to psychiatric practice. Proponents of an evidence-based approach to psychiatry hope that if practice is driven by "hard" scientific data, there will be greater potential to help patients. In other words, advocates of evidence-based psychiatry aim to bolster psychiatry's ethical standing through scientific evidence. Can EBM provide this ethical substantiation to psychiatry? This article provides an overview of some of the main ethical issues within psychiatry and examines three interrelated questions: (1) to which ethical values is EBM committed? (2) which ethical theory is reflected in these values? and (3) can these values and theories resolve existing ethical issues in psychiatry? EBM strives for the "greatest good for the greatest number," where good is defined as improved health. This utilitarian orientation cannot, however, address critical areas of moral importance for psychiatry, such as how its practitioners differentiate normal from abnormal, how they determine which forms of suffering should be alleviated through psychiatric means, and when involuntary intervention is ethically justified. The ethical principles implicit in EBM are too limited to serve as an ethical basis for psychiatry.
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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 | 0.096 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.080 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.015 | 0.016 |
| 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".