International Society of Psychiatric Genetics Ethics Committee: Issues facing us
Bibliographic record
Abstract
Psychiatric genetics research is improving our understanding of the biological underpinnings of neurodiversity and mental illness. Using psychiatric genetics in ways that maximize benefits and minimize harms to individuals and society depends largely on how the ethical, legal, and social implications (ELSI) of psychiatric genetics are managed. The International Society of Psychiatric Genetics (ISPG) is the largest international organization dedicated to psychiatric genetics. Given its history, membership, and international reach, we believe the ISPG is well-equipped to contribute to the resolution of these ELSI challenges. As such, we recently created the ISPG Ethics Committee, an interdisciplinary group comprised of psychiatric genetics researchers, clinical geneticists, genetic counselors, mental health professionals, patients, patient advocates, bioethicists, and lawyers. This article highlights key ELSI challenges identified by the ISPG Ethics Committee to be of paramount importance for the ethical translation of psychiatric research into society in three contexts: research settings, clinical settings, and legal proceedings. For each of these arenas, we identify and discuss pressing psychiatric genetics ELSI dilemmas that merit attention and require action. The goal is to increase awareness about psychiatric genetics ELSI issues and encourage dialogue and action among stakeholders.
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 | 0.031 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".