The contribution of <i>BJPsych Open</i> to the growing relevance of legal epidemiology
Bibliographic record
Abstract
Legal epidemiology is an emerging field that examines how laws and policies influence human rights and health outcomes, particularly in areas such as in-patient psychiatric treatment, community treatment orders and child maltreatment, This editorial highlights contributions from BJPsych Open that apply legal epidemiological methods to assess issues relevant to child maltreatment and coercion in psychiatric care. Findings emphasise the need for early intervention, standardised evaluation measures and reforms that prioritise human rights and well-being. Legal epidemiology can offer a scientific basis for improving legal frameworks, as well as promoting equitable and effective mental healthcare.
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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 | 0.018 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".