Public Mental Health Ethics: Helping Improve Mental Health for Individuals and Communities
Bibliographic record
Abstract
The burdens of mental illnesses and substance use disorders do not lie merely with the individuals who suffer from these conditions but affect, and are affected by, their families, communities, cities and countries. The ethical and political challenges that arise in the treatment of mental illnesses and substance abuse disorders are, therefore, challenges that affect both individuals and communities. In this symposium of Public Health Ethics, we attempt to concretize a burgeoning field of inquiry within public health ethics that focuses on mental health. ‘Public mental health ethics’ (PMHE) identifies and analyses ethical and political challenges as they relate to (i) the promotion of mental health in populations and (ii) the population-level prevention and treatment of mental illnesses and substance use disorders. PMHE prioritizes the ethical analysis of public health, policy and social care activities that are needed to reduce the burden of mental illness and substance use disorders. Although interested in ethical challenges that individuals with these conditions may face in relation to accessing and receiving routine health and medical care, PMHE focusses on the broader policy and programmatic context within which such care is delivered and accessed.
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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 | 0.049 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.019 | 0.026 |
| 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".