Bibliographic record
Abstract
First appearing in the published medical literature in 1992, evidence-based medicine (EBM) promotes a seemingly irrefutable principle: that clinical decision-making should be based, as much as possible, on the most up-to-date research findings. Nowhere has this idea been more welcome than in psychiatry, a field whose practices continue to be dogged by a legacy of controversial clinical interventions. For advocates, anchoring psychiatric practice in research data makes psychiatry more scientifically valid (meaning more accurate and value-neutral) and, as a result, more ethically legitimate. But because EBM makes certain assumptions about the nature of disease and treatment that may not apply to psychiatric disorders, it has also provoked vigorous debate in the field. This debate illustrates that rather than being value-neutral, EBM brings its own ethical values into practice. Are these the right values for psychiatry? The goal of this chapter is to stimulate reflection about this question.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".