Ethical challenges in developing drugs for psychiatric disorders
Bibliographic record
Abstract
As the classification of mental disorders advances towards a disease model as promoted by the National Institute of Mental Health (NIMH) Research Domain Criteria (RDoC), there is hope that a more thorough neurobiological understanding of mental illness may allow clinicians and researchers to determine treatment efficacy with less diagnostic variability. This paradigm shift has presented a variety of ethical issues to be considered in the development of psychiatric drugs. These challenges are not limited to informed consent practices, industry funding, and placebo use. The consideration for alternative research models and quality of research design also present ethical challenges in the development of psychiatric drugs. The imperatives to create valid and sound research that justify the human time, cost, risk and use of limited resources must also be considered. Clinical innovation, and consideration for special populations are also important aspects to take into account. Based on the breadth of these ethical concerns, it is particularly important that scientific questions regarding the development of psychiatric drugs be answered collaboratively by a variety of stakeholders. As the field expands, new ethical considerations will be raised with increased focus on genetic markers, personalized medicine, patient-centered outcomes research, and tension over funding. We suggest that innovation in trial design is necessary to better reflect practices in clinical settings and that there must be an emphasized focus on expanding the transparency of consent processes, regard for suicidality, and care in working with special populations to support the goal of developing sound psychiatric drug therapies.
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".